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**COMMON CONCEPTS IN
STATISTICS**

*See also** ***Common Terms in Mathematics**; **Epidemiology**; **Genetic
Epidemiology Glossary**; and** ****Statistics and Graphics with R **

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of this page**

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**Absolute
risk**:
Probability of an event over a period of time; expressed as a cumulative
incidence like 10-year risk of 10% (meaning 10% of individuals in the group of
interest will develop the condition in the next 10 year period). It shows the
actual likelihood of contracting the disease and provides more realistic and
comprehensible risk than **relative risk**/**odds ratio**.

**Accuracy**: The degree
to which a parameter (like the **mean**) is immune systematic error** **or**
bias**. Accuracy is increased by f=good experimental design. Accuracy is
different from **precision **(which has to do with random variation).

**Addition
rule**:
The probability of any of one of several mutually exclusive events occurring is
equal to the sum of their individual probabilities. A typical example is the
probability of a baby to be homozygous or heterozygous for a Mendelian
recessive disorder when both parents are carriers. This equals to 1/4 + 1/2 =
3/4. A baby can be either homozygous or heterozygous but not both of them at
the same time; thus, these are mutually exclusive events (see also **multiplication
rule**).

**Adjusted
odds ratio**:
In a multiple logistic regression model where the response variable is the
presence or absence of a disease, an odds ratio for a binomial exposure
variable is an adjusted odds ratio for the levels of all other risk factors
included in a **multivariable model**. It is also possible to calculate the
adjusted odds ratio for a continuous exposure variable. An adjusted odds ratio
results from the comparison of two strata similar at all variables except
exposure (or the marker of interest). It can be calculated when stratified data
are available as contingency tables by **Mantel-Haenszel test**.

**Affected
Family-Based Controls (AFBAC) Method**: One of several
family-based association study designs (**Thomson, 1995**). This one uses
affected siblings as controls and examines the sharing between two affected
family members. The parental marker alleles not transmitted to an affected
child or never transmitted to an affected sib pair form the so-called affected
family-based controls (AFBAC) population. See also **HRR** and **TDT**
and **Genetic Epidemiology**.

**Age-standardized
rate**:
An age-standardized rate is a weighted average of the age-specific rates, where
the weights are the proportions of a standard population in the corresponding
age groups. The potential confounding effect of age is removed when comparing
age-standardized rates computed using the same standard population.

**Alternative
explanations (of observed relationships)**: A correlation does
not mean causation (even if it is statistically significant). An observed
relationship (association/correlation) can only be considered causal when
alternative explanations can be ruled out. These are *chance/random variation*;
*bias/confounding* and *reverse causation*. See **Bias & Confounding**.

**Alternative
hypothesis**:
In practice, this is the hypothesis that is being tested
in an experiment. It is the conclusion that is reached when a null hypothesis
is rejected. It is the opposite of null hypothesis, which states that there is
a difference between the groups or something to that effect.

**Analysis of molecular variance (AMOVA)**: A
statistical (analysis of variance) method for analysis of molecular genetic
data. It is used for partitioning diversity within and among populations using
nucleotide sequence or other molecular data. AMOVA produces estimates of
variance components and F-statistic analogs (designated as phi-statistics). The
significance of the variance components and phi-statistics is tested using a
permutational approach, eliminating the normality assumption that is
inappropriate for molecular data (**Excoffier, 1992**). AMOVA can be performed on **Arlequin**.
For examples, see **Roewer, 1996**; **Stead, 2003**; **Watkins, 2003**).

**ANCOVA**: See **covariance models**.

**ANOVA** (analysis
of variance): A test for significant differences between multiple means by
comparing variances. It concerns a normally distributed response (outcome)
variable and a single categorical explanatory (predictor) variable, which
represents treatments or groups. ANOVA is a special case of multiple regression
where indicator variables (or orthogonal polynomials) are used to describe the
discrete levels of factor variables. The term analysis of variance refers not
to the model but to the method of determining which effects are statistically
significant. Major assumptions of ANOVA are the homogeneity of variances (it is
assumed that the variances in the different groups of the design are similar)
and normal distribution of the data within each treatment group. Under the null
hypothesis (that there are no mean differences between groups or treatments in
the population), the variance estimated from the within-group (treatment)
random variability (**residual sum of squares** = RSS) should be about the
same as the variance estimated from between-groups (treatments) variability (**explained
sum of squares** = ESS). If the null hypothesis is true, mean ESS / mean RSS
(variance ratio) should be equal to 1. This is known as the **F test** or
variance ratio test (see also **one-way** and **two-way ANOVA**). The
ANOVA approach is based on the partitioning of sums of squares and degrees of
freedom associated with the response variable. ANOVA interpretations of main
effects and interactions are not so obvious in other regression models. An
accumulated ANOVA table reports the results from fitting a succession of
regression models to data from a factorial experiment. Each main effect is
added on to the constant term followed by the interaction(s). At each level an
F test result is also reported showing the extra effect of adding each variable
so it can be worked out which model would fit best. In a two-way ANOVA with
equal replications, the order of terms added to the model does not matter,
whereas, this is not the case if there are unequal replications. When the
assumptions of ANOVA are not met, its non-parametric
equivalent **Kruskal-Wallis test** may be used (a review by ; a tutorial on **ANOVA posttest; online calculators for ANOVA (1),
(2),
(3)** and **(4;**
for analysis of summary data). See also **MANOVA**.** **

**Arithmetic
mean**:
M = (x_{1} + x_{2} + .... x_{n}) / n (n = sample size).

**Association**: A statistically significant
correlation or relationship between an environmental exposure or a
biochemical/genetic marker and a disease or condition. An association may be an
artifact (due to random sampling error-chance, bias, confounding) or a real
one. In population genetics, an association may be due to **population
stratification**, **linkage disequilibrium**, or direct causation. A
significant association should be presented together with a measure of the
strength of association called **effect size** (**odds ratio**, **relative
risk** or **hazard** **ratio** and its 95% confidence interval) and
when appropriate a measure of potential impact (**attributable risk,** **prevented
fraction, attributable fraction/etiologic fraction**).

**Assumptions**: Certain
conditions of the data that are required to be met for validity of a
statistical test. **ANOVA** generally assumes normal distribution of the
data within each treatment group, homogeneity of the variances in the treatment
groups, and independence of the observations. In **regression analysis**,
main assumptions include the normal distribution of the response variable, constant variance across fitted values,
independence of **error terms**, and the consistency of underlying hazard
rate over time (**proportionality assumption**) in **Cox
Proportional Hazard Model****s**.

**Asymptotic**: Refers to
a curve that continually approaches either the x or y axis but does not
actually reach it until x or y equals infinity. The axis so approached is the
asymptote. An example is the **normal distribution curve**.

**Asymptotically
unbiased**:
In point estimation, the property that the bias approaches zero as the sample
size (N) increases. Therefore, estimators with this property improve as N
increases. See also **bias**.

**Attributable
risk (AR)**:
Also called excess risk or risk difference. A measure of potential impact of an
association. It quantifies the additional risk of disease following exposure
over and above that experienced by individuals who are not exposed. It shows
how much of the disease is eliminated if no one had the risk factor
(unrealistic). The information contained in AR combines the **relative risk**
and the risk factor prevalence. The larger the AR, the greater the effect of
the risk factor on the exposed group. See also **prevented fraction**, **Walter, 1978** and
**Attributable Risk Applications in Epidemiology**.
For online calculation, see **EpiMax Table Calculator**.

**Attributable
fraction (etiologic fraction)**: It shows what proportion of disease in the
exposed individuals is due to the exposure.

**Balanced
design**:
An experimental design in which the same number of observations is taken for
each combination of the experimental factors.

**Bartlett’s
test**:
A test for homogeneity of variance.

**Bayesian
inference**:
An inference method radically different from the classical frequentist approach
which takes into account the prior probability for an event. Established as a
new method by **Reverend Thomas Bayes**. See **+Plus**: **Bayesian Statistics Explained**; **MathWorld**: **Bayesian Analysis**; **Bayesian Calculator (1)** & (2).

**Bayes'
method in genetic counseling**: This method uses available additional
information to modify risks calculated purely by Mendelian probabilities. It
combines prior and conditional probabilities to give joint and posterior
probabilities of unknown events. See **Bayesian Analysis and Risk Assessment in Genetic Counseling
and Testing**.

**Bernoulli
distribution** models the behavior of data taking just two distinct
values (0 and 1).

**Bias**: In
statistics, an estimator for a parameter is unbiased if its expected value is
the true value of the parameter. Otherwise, the estimator is biased. It is the
quantity E = (q -hat) - q. If the
estimate of q is the same
as actual but unknown q, the estimate is unbiased (as in estimating
the mean of normal, binomial and Poisson distributions). If bias tends to
decrease as n gets larger, this is called **asymptotic unbiasedness**. In
epidemiology, bias is any deviation from truth (intentional bias is scientific
fraud). See reviews on epidemiologic meaning of bias: **Bias & Confounding in Molecular Epidemiology**; **Bias and
Confounding Lecture Note**; a **Guide to Bias**; and **ERIC Notebook**.

**Binary
(dichotomous) variable**: A discrete random variable that can only take
two possible values (success or failure).

**Binomial
distribution**: The binomial distribution gives the probability of
obtaining exactly *r* successes in *n*
independent trials, where there are two possible outcomes one of which is
conventionally called success (**Online
Binomial Test** for observed vs expected value; **Binomial
Probability Calculator**).

**Blocks**:
Homogeneous grouping of experimental units (subjects) in experimental design.
Groups of experimental units that are relatively homogeneous in that the
responses on two units within the same block are likely to be more similar than
the responses on two units in different blocks. Dividing the experimental units
into blocks is a way of improving the accuracy between treatments. Blocking
will minimize variation between subjects that is not attributable to the
factors under study. Blocking is similar to matching in two-sample tests or
stratification to control for confounding.

**Blocking**: When the
available experimental units are not homogeneous, grouping them into blocks of
homogeneous units (stratification) will reduce the experimental error variance.
This is called blocking where differences between experimental units other than
those caused by treatment factors are taken into account. This is like
comparing age-matched groups (blocks) of a control group with the corresponding
blocks in the patients group in an investigation of the side effects of a drug
as age itself may cause differences in the experiment. Block effects soak up
the extra, uninteresting and already known
variability in a model. Blocking is preferable to randomization when the
factors that might affect the outcome are known.

**Bonferroni Correction**: This is a multiple
comparison technique used to adjust the (type I) a
error level. See also **HLA and Disease Association Studies**, **Online Bonferroni Correction** &** a
commentary by Perneger, 1998**).

**Bootstrap**: An application of resampling
statistics. It is a data-based simulation method used to estimate variance and
bias of an estimator and provide confidence intervals for parameters where it
would be difficult to do so in the usual way (**Online
Resampling Book**).

**Canonical**: Something that has been reduced
to its simplest form.

**Carryover effect**: Any effect of a drug that lasts
beyond the period of treatment. This is a worry in drug trials with **crossover
design** and the reason for the washout period between treatments.

**Case-control study**: A design preferred over
cohort studies for relatively rare diseases in which cases with a disease or
exposure are compared with controls randomly selected from the same study base.
This design yields **odds ratio** as opposed to **relative risk** from
cohort studies. See **Case-control Studies Chapter** in **Epidemiology for the Uninitiated**.

**Causal relationship**: It does not matter how
small it is, a *P* value does not signify causality. To establish a causal
relationship, the following non-statistical evidence is required: consistency
(reproducibility), biological plausibility, dose-response, temporality (when
applicable) and strength of the relationship (as measured by an **effect size**
such as **odds ratio**/**relative risk**/**hazard ratio**). See **Hills's criteria of causality**; **Seven Common Errors in Statistics**;
and Causality by DR Cox, JR Stat Soc A 1992;155:291 (**JSTOR-UK link**). The original reference for Hill's criteria is Hill AB: The
environment and disease: association or causation. Proc R Soc Medicine 1965; 58:295-300.

**Categorical
(nominal) variable**: A variable that can be assigned to
categories. A non-numerical (**qualitative**) variable measured on a
(discrete) **nominal** scale such as gender, drug treatments, disease
subtypes; or on an **ordinal** scale such as low, median or high dosage. A
variable may alternatively be **quantitative** (**continuous** or **discrete**).
See **GraphPad
QuickCalc**: **Categorical
Data Analysis**.

**Censored
observation**: Observations that survived to a certain point in time
before dropping out from the study for a reason other than having the outcome
of interest (lost to follow up or not enough time to have the event). Thus,
censoring is simply an incomplete observation that has ended before
time-to-event. These observations are still useful in **survival analysis**.

**Central limit theorem**: The means of a relatively
large (>30) number of random samples from any population (not necessarily a
normal distribution) will be approximately normally distributed with the
population mean being their mean and variance being the (population variance /
n). This approximation will improve as the sample size (the number of samples)
increases. See **Mathematical Basis**; **QuickTime Demonstration**;** JAVA Demonstration**; **Simulation**.

**Chi-squared distribution**: A
distribution derived from the **normal distribution**. Chi-squared (C^{2})
is distributed with v degrees of freedom with mean = v and variance = 2v (**Chi-Square to
P Calculator**).

**Chi-squared test**: The most commonly used test for
frequency (categorical) data analysis and as a goodness-of-fit test. In theory,
it is nonparametric but because it has no parametric equivalent, it is not
classified as such. It is not an exact test and with the current level of
computing facilities, there is not much excuse not to use Fisher’s exact test
for 2x2 contingency table analysis instead of Chi-squared test. Also, for
larger contingency tables, the G-test (log-likelihood ratio test) may be a
better choice. The Chi-square value is obtained by summing up the values
(residual^{2}/expected) for each cell in a contingency table. In this
formula, residual is the difference between the observed value and its expected
counterpart. See **Statistical Analysis in HLA and Disease Association Studies**
for assumptions and restrictions of the Chi-squared test (**Chi-squared
tests of association**; **Tables
of critical values of t, F and Chi-square**; **Chi-square to
P calculator; Chi-squared test with GraphPad
QuickCalc**;

**Cochran's Q Test**: A nonparametric test examining
change in a dichotomous variable across more than two observations. If
there are two observations, **McNemar's test** should be used.

**Coefficient
of determination (R ^{2})**: See

**Coefficient
of variation (CV)**: It is a measure of spread for a set of data
(variation in relation to the mean). Calculated as standard deviation divided
by the mean (x100), which expresses the standard deviation as a percentage of
the mean. It allows the comparison of the variation in two different datasets.
For example, comparing two datasets for the same temperature data measured in
Fahrenheit and Celsius would show greater variation for Fahrenheit data if
standard deviation is used, but shows equal variation with CV. Similarly, if
one wonders whether height shows more variation in males or females, standard
deviation would be misleading, but CV will provide the correct comparison. (**Online Calculator for Coefficient of Variation and Other
Descriptive Statistics**).

**Cohort effect**: The
tendency for persons born in certain years to carry a relatively higher or
lower risk of a given disease. This may have to be taken into account in
case-control studies.

**Concomitant
variable**:
See **covariance models**.

**Conditional
(fixed-effects) logistic regression**: The conditional
logistic regression (CLR) model is used in studies where cases and controls can
be matched (as pairs) with regard to variables believed to be associated with
the outcome of interest. The model creates a likelihood that conditions on the
matching variable. It is the preferred method for the analysis of nested
case-control studies when matching is done at the individual level (there may
be more than one control per case). In economic analysis, it is called
fixed-effects logit for panel data. See **Preisser & Koch, 1997**.

**Confounding
variable**:
A variable that is associated with both the outcome and the exposure variable (also
called a lurking variable). A classic example is the relationship between heavy
drinking and lung cancer. Here, the data should be controlled for smoking as it
is related to both drinking and lung cancer. A positive confounder is related
to exposure and response variables in the same direction (as in smoking); a
negative confounder shows an opposite relationship to these two variables (age
in a study of association between oral contraceptive use and myocardial
infarction is a negative confounder). The data should be stratified before
analyzing it if there is a confounding effect. **Mantel-Haenszel** test is
designed to analyze stratified data to control for a confounding variable.
Alternatively, **a
multivariable regression model** can be used to adjust for
the effects of confounders. Despite all efforts to control for unknown
confounders, there may always be some residual confounding left due to unknown
confounders. To minimize the amount of confounding due to known (like
age, sex, ethnicity) or unknown (genetic, behavioral, cultural) confounders,
randomization is an option (and the reason for randomized controlled trials to
be the gold standard in establishing causality). See **Bias & Confounding
Lecture Note**; a review by **Greenland, 2001**; a **Guide to Confounding**; and **ERIC Notebook**.

**Conservative
test**:
A test where the chance of type I error a (false-positive) is
reduced and type II error b (false-negative) risk is increased. Thus,
these tests tend to give larger (less significant) *P* values compared to
non-conservative (liberal) tests for the same comparison.

**Contrast**: A contrast
is combinations of treatment means, which is also called the main effect in **ANOVA**.
It measures the change in the mean response when there is a change between the
levels of one factor. For example, in an analysis of three different
concentrations of a growth factor on cell growth in cell culture with means m_{1}, m_{2}, m_{3}, against a
control value (m_{o}) without
any growth factor, a contrast would be:

q = m_{o} - 1/3 (m_{1}+ m_{2}+ m_{3})

The important point is that the coefficients sum to zero (1/1 - 1/3 - 1/3 - 1/3). If the value of the contrast (q) is zero or not significantly different from zero, there is no main effect, i.e., the combined growth factor mean is not different (positive or negative) from the no growth factor mean.

**Controlled
experiment**:
In a controlled experiment, all external variables are fixed and a single
exposure (independent) variable is varied among the participants to measure its
effect on the outcome (dependent) variable. This way, the researcher can be
more confident that the observed relationship is attributable to the exposure
(and not confounded). Since complete control of external variables is not
always possible in human studies, random allocation (**randomization**) can
be used to attempt equal distribution of uncontrollable and unmeasured
variables between the comparative groups. A typical example of a human
controlled experiment is a randomized controlled/clinical trial (**RCT**). The
examination of the effect of sun exposure on plant growth by placing the same
plants grown in the same soil in a cupboard and another on the window sill, and
keeping other exposures the same (like watering) is a prototype controlled
experiment.

**Cook
statistics**:
A diagnostic *influence* statistics in regression analysis designed to
show the influential observations. **Cook's distance** considers the
influence of the *i* th value on all n fitted values and not on the fitted
value of the *i *th observation. It yields the shift in the estimated
parameter from fitting a regression model when a particular observation is
omitted. All distances should be roughly equal; if not, then there is reason to
believe that the respective case(s) biased the estimation of the regression
coefficients. Relatively large Cook statistics (or Cook's distance) indicates
influential observations. This may be due to a high **leverage**, a large **residual**
or their combination. An **index plot** of residuals may reveal the reason
for it. The leverages depend only on the values of the explanatory variables
(and the model parameters). Cook statistics depends on the residuals as well. Cook statistics may not be very satisfactory in
binary regression models. Its formula uses the standardized residuals but the
modified Cook statistics uses the **deletion residuals**.

**Correlation Coefficient**: See **Pearson's
correlation coefficient (r)**, **Spearman’s rank correlation (rho)** and **Multiple regression correlation coefficient (R ^{2})**.
(

**Correspondence
analysis**:
In population genetics, a complementary analysis to
genetic distances and dendrograms. It displays a global view of the
relationships among populations (**Greenacre MJ, 1984; Greenacre & Blasius, 1994; Blasius & Greenacre, 1998**). With its
visual output, it supplements more formal inferential analyzes. This type of
analysis tends to give results similar to those of dendrograms as expected from
theory (**Cavalli-Sforza & Piazza, 1975**), but is
more informative and accurate than dendrograms especially when there is
considerable genetic exchange between close geographic neighbors (**Cavalli-Sforza et al. 1994**). Cavalli-Sforza
et al concluded in their enormous effort to work out the genetic relationships
among human populations that two-dimensional scatter plots obtained by
correspondence analysis frequently resemble geographic maps of the populations
with some distortions (**Cavalli-Sforza et al. 1994**). Using the same
allele frequencies that are used in phylogenetic tree construction, **correspondence analysis** can be performed on **ViSta, VST, Statistica,
SAS **but most conveniently** **on MultiVariate
Statistical Package (**MVSP**). Link to **a
Tutorial**; **StatSoft
Textbook**: **Correspondence
Analysis Chapter**.

**Cox
proportional hazards model**: A regression method described by D.R. Cox (J
Royal Stat Soc, Series B 1972;34:187-220; **JSTOR-UK**) for modeling survival times
(for significance of the difference between survival times, **log-rank test **is
used). It is also called proportional hazards model because it estimates the
ratio of the risks (**hazard ratio **or **relative hazard**). As in any
regression model, there are multiple predictor variables (such as prognostic
markers whose individual contribution to the outcome is being assessed in the
presence of the others) and the outcome variable
(e.g., whether the patients survived five years, or died during follow-up,
etc). The model assumes that the underlying hazard rate (rather than survival
time) is a function of the independent variables and consistent over time (**proportionality assumption**, i.e. the
survival functions of the groups are approximately parallel). There is no
assumption for the shape and nature of the underlying survival function.
Cox's regression model has been the most widely used
method in survival data analysis regardless of whether the survival time is
discrete or continuous and whether there is censoring (**Lee & Go, 1997**). Cox regression uses the
**maximum likelihood method**
rather than the **least squares method** (**Superlectures** on survival analysis).

**Covariate
(covariable)**: Generally used to mean any explanatory variable, less
generally, an additional explanatory variable that is not of main interest but
included in the model to adjust the statistical association of the main
explanatory variable. The intention is to produce more precise and adjusted
estimates of the association of the explanatory variable of main interest. In
the analysis, a model is first fitted using the covariate. Then the main
explanatory variable is added and its additional effect is assessed statistically.
Whether the use of a covariate is wise (i.e., whether it has a statistically
significant influence) can be judged by checking its effect on the residual
(error) mean square (variance). If the addition of covariate reduces it
remarkably, it will improve the analysis. See also **covariance models**.

**Covariance
(covariation)**: It is a measure of the association between a pair of
variables: the expected value of the product of the deviation of two random
variables from their respective means. It is also called a measure of ‘linear
dependence’ between the two random variables. If the two variables are
independent (no linear correlation), then their covariance is zero but for
non-zero values covariance is unstandardized (unlike **correlation coefficient**);
there is no limit to possible values. Because of this, it is difficult to
compare covariances. A negative value means that for small values of X, there
are large values of Y (inverse association). It is calculated as the mean
sum-of-products using each x_{i} and y_{i} values,
their means m_{x} and m_{y}, and (n-1).
(The covariance standardized to lie between -1 and +1 is **Pearson’s
correlation coefficient**.) See **Wikipedia**:
**Covariance**;
**NetMBA
Statistics**: **Covariance**.

**Covariance
models**:
Models containing some quantitative and some qualitative explanatory variables,
where the chief explanatory variables of interest are qualitative and the
quantitative variables are introduced primarily to reduce the variance of the
error terms. [Models in which all explanatory variables are qualitative are
called analysis of variance -**ANOVA**- models.] Analysis of covariance
-ANCOVA- combines features of ANOVA and regression. It augments the ANOVA model
containing the factor effects with one or more additional quantitative
variables that are related to the response variable. The intention is to make
the analysis more precise by reducing the variance of the error terms. Each
continuous quantitative variable added to the ANOVA model is called a **concomitant
variable** (and sometimes also covariates). If qualitative variables are
added to an ANOVA model to reduce error variance, the model remains to be
ANOVA. By adding extra variables, the results are said to be controlled or
adjusted for the additional variables (like age or sex).

Apart from the above use of the term, analysis of covariance is more generally used for almost any analysis assessing the relationship between a response variable and a number of explanatory variables. In a multiple regression model, additional variables, which are known not to have any effect on the response variable, such as age and sex, are sometimes added to the model to adjust the response for these variables (age and sex in this case). Such variables are called confounders (or covariates). When the response is normally distributed, this is the preferred method over a simple t-test when the two groups compared differ, say, in their age and sex distribution (or any other confounding variable). The result is then controlled (or adjusted) for age and sex. When such adjustments are made, the regression coefficient for the significant effect variable will be (most probably) different from the one obtained from a univariable model involving only that variable (say the effect of a disease on pulse rate as compared to healthy controls).

**Cramer’s
coefficient of association** **(C)**: Also known as contingency
coefficient. While Chi-squared is used to determine significance of an
association (and varies by sample size for the same association), Cramer’s C is
a measure of association varying from 0 (no association) to 1 (perfect
association) that is independent of the sample size. However, it seldom reaches
its upper limit. It allows direct comparison of the degree of association
between different contingency tables. It is calculated directly from the
Chi-squared value and the total sample size as (C^{2}/C^{2}+N) ^{½}.

**Cramer’s
V**:
A measure of the strength association for any size of contingency tables. It
can be seen as a correction of the Chi-squared value for sample size. The
transformation of the chi-squared value provides a value between 0 and 1 for
relative comparison of the strength of the association. For a 2x2 table,
Cramer's V is equal to the **Phi coefficient**. Cramer’s V is most
useful for large contingency tables. It can also be used as a global linkage
disequilibrium value for multiallelic loci (See **GOLD-Disequilibrium Statistics**;
Online **Cramer’s
V calculation**.)

**Crossover design**: A clinical trial design during
which each subject crosses over from receiving one treatment to another one.

**Cross-sectional data**: Data collected at one
point in time (as opposed to longitudinal/cohort data for example). See **Cross-Sectional Studies Chapter** in **Epidemiology for the Uninitiated**.

**Degrees
of freedom (df)**: The number of independent units of information in a
sample used in the estimation of a parameter or calculation of a statistic. In
the simplest example of a 2x2 table, if the marginal totals are fixed, only one
of the four cell frequencies is free to vary and the others will be dependent
on this value not to alter the marginal totals. Thus, the df is only 1.
Similarly, it can easily be worked out that in a contingency table with r rows
and c columns, the df = (r-1)(c-1). In parametric tests, the idea is slightly
different that the n bits of data have n degrees of freedom before we do any
statistical calculations. As soon as we estimate a parameter such as the mean,
we use up one of the df, which was initially present. This is why in most
formulas, the df is (n-1). In the case of a two-sample t-test with n_{1}
and n_{2} observations, to do the test we calculate both means. Thus,
the df = (n_{1 }+ n_{2 }- 2). In linear regression, when the
linear equation y = a +
bx is
calculated, two parameters are estimated (the intercept and the slope). The df
used up is then 2: df = (n_{1 }+ n_{2 }- 2). Non-parametric
tests do not estimate parameters from the sample data, therefore, df is not an
issue with them them.

In
simple linear regression, the df is partitioned similar to the total sum of
squares (**TSS**). The df for TSS is N-k. Although there are n deviations,
one df is lost due to the constraint they are subject to: they must sum to
zero. TSS equals to **RSS** + **ESS**. In one-way ANOVA, the df for RSS
is N-2 because two parameters are estimated in obtaining the fitted line. ESS
has only one df associated with it. This is because the n deviations between
the fitted values and the overall mean are calculated using the same estimated
regression line, which is associated with two df (see above). One of them is
lost because the of the constraint that the deviations must sum to zero. Thus,
there is only one df associated with ESS. Just like TSS = RSS + ESS, their df
have the same relationship: N-1 = (N-2) + 1.

**Deletion
(or deleted) residual**: A modified version of the standardized
residual, which uses an estimate of s^{2} from a regression in
which point *i* has been deleted. It is used in calculation of the
modified **Cook's distance** instead of standardized residuals. Deletion
residuals are also known as **likelihood residuals**.

**Descriptive
statistics**:
Summary of available data. Examples are male-to-female ratio in a group;
numbers of patients in each subgroup; the mean weight of male and female
students in a class, etc. Only when the distribution is symmetric, mean and
standard deviation can be used. Otherwise (as in survival data), mean and
percentiles/range should be used to describe the data. See **GraphPad Guide** to **Interpreting Descriptive Statistics**.

**Deviance**: A measure
for judging the degree of matching of the model to the data when the parameter
estimation is carried out by maximizing the likelihood (as in GLMs). The
deviance has asymptotically a Chi-squared distribution with df equal to the
difference in the number of parameters in the two models being compared. The
total deviance compares the fit of the saturated model to the null model, thus,
expresses the total variability around a fitted line which can be decomposed to
explained and unexplained (error) variability. The *residual deviance* in
a GLM analysis of deviance table corresponds to RSS in an ANOVA table, *and
regression deviance* corresponds to ESS. The residual deviance measures how
much fit to the data is lost (in likelihood terms) by modeling compared to the
saturated model. This will be a small value if the model is good (and it will
be zero for the saturated model containing all main effects and all
interactions). The regression deviance measures how much better is the model
taking into account the explanatory variables compared to the simplest model
ignoring all of them and only containing a constant (the mean of the
responses). This measurement is again made in terms of log-likelihood and the
bigger the regression deviance the better fits the model (i.e., the regression
effect is strong). In likelihood terms, the *residual* deviance can be expressed
as follows:

*D* = -2 [*ln*
L_{c} - *ln* L_{s}] or *D* = -2 *ln* [L_{c}
/ L_{s}]

where
L_{c} is the likelihood of the current model, and L_{s} is the
likelihood of the saturated model.

Similarly,
the *regression* deviance can be expressed as:

*D* = -2 [*ln*
L_{c} - *ln* L_{n}] or *D* = -2 *ln* [L_{c}
/ L_{n}]

where
L_{n} is the
likelihood of the null model. The bigger the regression deviance the better the
model including this particular variable.

For
purposes of assessing the significance of an independent variable, the value of
*D* with and without the independent variable is compared (note the nested
character of the two sets). This is called the **deviance difference**. If a
variable is dropped, the residual deviance difference, if a variable is added,
the regression deviance difference is compared with the C^{2}
distribution.

**Deviance
difference**:
In generalized linear modeling, models that have many explanatory variables may
be simplified, provided information is not lost in this process. This is tested
by the difference in deviance between any two nested models being compared:

*G* = *D*
(for the model without the variable) - *D* (for the model with the
variable)

The
smaller the difference in (residual) deviance, the smaller the impact of the
variables removed. This can be tested by the C^{2} test.

In
the simplest example (in simple linear regression), if the log-likelihood of a
model containing only a constant term is L_{0} and the model containing
a single independent variable along with the constant term is L_{1},
multiplying the difference in these log-likelihoods by -2 gives the deviance
difference, i.e., *G* = -2 (L_{1} - L_{0}). *G*
statistics (**likelihood ratio test**) can be compared with the C^{2}
distribution on df = 1, and it can be decided whether the model with the
independent variable has a regression effect (if *P* < 0.05, it has).
The same method can be used to detect the effect of the interaction by adding
any interaction to the model and obtaining the regression deviance. If this
deviance is significantly higher than the one without the interaction in the
model, there is interaction [the coefficient for the interaction, however, does
not give the **odds ratio** in **logistic regression**].

**Discrete
variable**:
A variable of countable number of integer outcomes. Examples include -**ordinal
multinomial**- several prognostic outcomes (such as poor, median and good) as
a function of treatment modalities, stage of the disease, age etc., or -**multinomial**-
people’s choices of hospitals (hospital A, B or C) as a function of their
income level, age, education etc. A discrete variable may be **binomial**:
diseased or non-diseased in a cohort or case-control study. The nature of the
outcome variable as discrete or continuous is crucial in the choice of a
regression model (see the **generalized linear model**).

**Dummy
variables**:
A binary variable that is used to represent a given level of a **categorical
variable**. In genetic data analysis, for example, it is created for each
allele at a multiallelic locus. The most common choices as dummy variables are
0 and 1. For each level of the variable, 1 denotes having the feature and 0
denotes all other levels. Also called indicator variables. If an indicator
variable is used, the regression coefficient gives the change per unit compared
to the reference value. Creating dummy variables on Stata: **Stata** @ **UCLA Statistics**: **STATA Dummy Variables**.

**Dunn's
Test**:
This test is used when a difference between the groups is found in a
non-parametric ANOVA test. Dunn's test is a **post hoc test** that makes
pairwise (multiple) comparisons to identify the different group. See **GraphPad Prism Guide**: **K-W and
Dunn's Test; StatTools: Dunn's Test**).

**Dunnett's
test**:
When ANOVA shows a significant difference between groups, if one of the groups
is a control (reference) group, Dunnett's Test is used as a ** post hoc
test**. This multiple comparison test can be used to determine the
significant differences between a single control group mean and the remaining
treatment group means in an analysis of variance setting. It is one of the
least conservative

**Ecological
study**:
Analyses based on data grouped to the municipal, provincial or national level. See **Ecological Studies Chapter** in **Epidemiology for the Uninitiated**.

**Ecological
fallacy: **The
aggregation bias, which is the unfortunate consequence of making inferences for
individuals from aggregate data. It results from thinking that relationships
observed for groups necessarily hold for individuals. The problem is that it is
not valid to apply group statistics to an individual member of the same group.
See an essay on **Ecological Fallacy**.

**Edwards' test**: A statistical test for seasonality that looks for a one-cycle sinusoidal
deviation from the null distribution (see **Westerbreek et al, 1998**).

**Effect modification**: The situation in which a
measure of effect changes over values of another variable (the association
estimates are different in different subpopulations of the sample). The
relative risk or odds ratio associated with exposure will be different
depending on the value of the effect modifier. For example if in a disease
association study, the odds ratios are different in different age groups or in
different sexes, age or sex are effect modifiers. Effect modification is highly
related to statistical interaction in regression models. If where an exposure
decreases risk for one value of the effect modifier and increases risk for
another value of effect modifier, this is called crossover. See **Thompson, 1991** and *Effect Modification*
in __ Encyclopedia of
Biostatistics__.

**Effect size**: In statistics, effect size is the
measure of the strength of an association or effect. It is usually quantified
by calculation of **relative risk**, **odds ratio** or **hazard ratio**.
Effect size (together with its 95% confidence interval) complements the ** P
value** and should always accompany it when an association is reported. The
practice of exclusive reporting of the

**Eigenvalues** (latent values): In multivariate
statistics, eigenvalues give the variance of a linear function of the
variables. Eigenvalues measure the amount of the variation explained by each
principal component (PC) and will be largest for the first PC and smaller for
the subsequent PCs. An eigenvalue greater than 1 indicates that PCs account for
more variance than accounted by one of the original variables in standardized
data. This is commonly used as a cut-off point for which PCs are retained.

**EM
Algorithm**:
A method for calculating maximum likelihood estimates with incomplete data. E (expectation)-step
computes the expected values for missing data and M (maximization)-step
computes the maximum likelihood estimates assuming complete data. It was first
used in genetics (**Ceppellini R et al, 1955**) to estimate
allele frequency for phenotype data when genotypes are not fully observable
(this requires the assumption of HWE and calculation of expected genotypes from
phenotype frequencies). **Arlequin**
implements EM algorithm in haplotype construction and frequency analysis.

**Empirical P value**: A

**Empirical rule**: In variables normally
distributed, 68% of the data values are within 1SD of the mean; 95% are within
2SD of the mean; and 99.7% (nearly all) are within 3SD of the mean.

**Epidemiologic flaws and fallacies**: Beware of
confounders, selection bias, response bias, variable observer, Hawthorne effect
(changes caused by the observer in the observed values), diagnostic accuracy
bias, **regression to the mean**, significance Turkey,
nerd of nonsignificance, cohort effect, **ecological fallacy**, Berkson bias (selection
bias in hospital-based studies) and others (discussed in M Michael III et al. **Biomedical Bestiary**. Little, Brown and
Company, 1984; and in **Bias & Confounding in Molecular Epidemiology**).

**Epi-Info**: An epidemiologic data management
and analysis package freely available from the** CDC website**.
Originally a DOS program, later versions are designed for Microsoft Windows (**User Guide**; **Tutorial**).

**Error
terms**:
The difference between an observed/predicted value and the true value is the **e**rror
or **e**psilon (e)
in statistics. Error terms are shown as W_{i} or e_{i}. Their expected
value is zero (which is the assumption of many statistical tests especially in
regression analysis), thus, they vary around zero with a variance equal to s^{2}: N(0, s^{2}). As a
major assumption of the regression analysis, they are assumed to be normally
distributed, have equal variance for all fitted values, and independent.
Normality is a reasonable assumption in many cases. The assumption of equal
variance implies that every observation on the dependent variable contains the
same amount of information. The impact of heterogeneous variances is a loss of **precision**
of estimates compared to the **precision** that would have been realized if
the heterogeneous variances had been taken into account. Transformation of the
dependent variable may help to homogenize the unequal variances. Correlated
errors are most frequent in time sequence data and they also cause the loss in **precision**
in the estimates. See also **residuals**.

**Explained
(regression) sum of squares (ESS)**: The measure of between treatments sum
of squares (variability) in ANOVA. If the means of treatment groups are
different, the ESS would be greater than RSS to yield a high variance ratio.
The bigger the ESS, the better explained the data by the model.

**Exploratory
data analysis**: An initial look at the data with minimal use of formal
mathematics or statistical methods, but more with an informal graphical
approach. Scatter plots, correlation matrices and contingency tables (for
binary data) can be used to get an initial idea for relationships between explanatory
variables (for collinearity) or between an explanatory variable(s) and a
response variable(s) (correlation). In ANOVA, normality can be checked by
box-plots. It gives some indication of which variables should be in the model
and which one of them should be put into the model first, and whether linear
relationship is adequate.

**Exponential
distribution**: The (continuous) distribution of time intervals between
independent consecutive random events like those in a Poisson process.

**Exponential
family**:
A family of probability distributions in the form

f(x) = exp {a(q )b(x) + c(q ) + d(x)}

where
q is a
parameter and a, b, c, d are known functions. This family includes the **normal distribution**, binomial
distribution, Poisson distribution and gamma distribution as special cases.

**Exposure**: In an
epidemiologic study, exposure may represent an environmental exposure, an
intervention or the presence of a marker (biomarker/genetic marker).

**Factor**: A
categorical explanatory variable with small number of **levels** such that
each item classified belongs to exactly one level for that category. If the
factor is 'sex', the levels are 'male' and 'female'; if the factor is 'drug
received', the levels are 'drug A', 'drug B', 'drug C', etc. A set of factor
levels, uniquely defining a single treatment, is called a **cell**. A cell
may have just one observation (no replication) or multiple observations
(replications).

**Factorial
experiments**: In some data, the explanatory (predictor) variables are
all categorical (i.e., **factors**) and the response variable is
quantitative. When there are two or more categorical predictor variables, the
data are called **factorial**. The different possible values of the factors
are often assigned numerical values known as **levels**.

**Factorial
analysis of variance**: An analysis in which the treatments differ in
terms of two or more factors (with several levels) as opposed to treatments
being different levels of a single factor as in one-way ANOVA.

**False
discovery rate **(**FDR**): False discovery rate estimates the proportion
of false positive results among those tests that are being reported as
statistically significant. **Link** to a calculator (for 2x2
tables).

**Family-wise
error rate**
(**FWER**; designated as z): The type I error probability (a) for a
single, individual endpoint estimates the occurrence of a false-positive result
for a single analysis. The family-wise error level is about the occurrence of
at least one type I error in the entire set of analyses. Thus, FWER is the
probability that at least one type I error has occurred across all of the
analyses. Bonferroni correction for the number of comparisons is an example of
adjustment for the FWER (if corrected *P* values smaller than 0.05 are expected
as statistically significant, FWER is set at 0.05).

**F
distribution**: A continuous probability distribution of the ratio of two
independent random variables, each having a Chi-squared distribution, divided
by their respective degrees of freedom. The commonest use is to assign *P*
values to mean square ratios (variance ratios) in ANOVA. In regression
analysis, the F-test can be used to test the joint significance of all variables of a model. (**Tables
of critical values of t, F and Chi-square**).

**Fisher's exact test**: An exact
significance test to analyze 2x2 tables for any sample size. It is a
misconception that it is suitable only for small sample sizes. This arises from
the demanding computational procedure for large samples, which is no longer an
issue. It is the only test for a 2x2 table when an expected number in any cell
is smaller than 5 (**Online Fisher's Test (1)**;
**(2)**;
**Calculator
3** in **Clinical Research Calculators** at **Vassar**).
For an exact test for larger contingency tables, see **Vassar Online**
or download **RxC** by Mark Miller.

**F
test**:
The F test for linear regression tests whether the slope is significantly
different from 0, which is equivalent to testing whether the fit using non-zero
slope is significantly better than the null model with 0 slope. See also **mean
squares**.

**Gambler's
ruin**:
A classical topic in probability theory. It is a game of chance related to a
series of Bernoulli trials. There are variations of the game theory associated
with problems of the random walk and sequential sampling.

**Game
theory**:
The theory of contests between two or more players under specified sets of
rules. The statistical aspect is that the game proceeds under a chance scheme
such as throwing a die.

**Gaussian
distribution**: Another name for the **normal distribution** (**GraphPad Gaussian Distribution Calculator**).

**G Statistics**: An application of the **log-likelihood
ratio statistics** for the hypothesis of independence in an *r* x *c*
contingency table. It can also be used to test goodness-of-fit. The G-test
should be preferred over Chi-squared test when for any cell in the table, ½
O-E½ > E. The Chi-squared distribution is usually poor for the test
statistics G^{2} when N/rc is smaller than five (preferable to the
Chi-squared test in Hardy-Weinberg Equilibrium (HWE) test as long as this
condition is met). **HyperStat**
and **StatXact**
perform G statistics (**Online G
Statistics**).

**General
linear model**: A group of linear regression models in which the response
variable is continuous and normally distributed, the response variable values
are predicted from a linear combination of predictor variables, and the linear
combination of values for the predictor variables is not transformed (i.e.,
there is no **link function** as in **generalized linear models**).
Linear multiple regression is a typical example of general linear models
whereas simple linear regression is a special case of **generalized linear
models** with the identity link function.

**Generalized
linear model (GLM)**: A model for linear and non-linear effects of
continuous and categorical predictor variables on a discrete or continuous but
not necessarily normally distributed dependent (outcome) variable. (Note that
in the **general linear model**, the dependent (outcome) variable should be
normally distributed). Normal, binary (or linear logistic; when the outcome
variable is a proportion), binomial or Poisson (when the outcome variable is a
count), exponential and gamma (when the outcome variable is continuous and
non-negative) models are different versions of generalized linear models.
Particular types of models arise by specifying an appropriate ** link
function**, variance and distribution. For example, normal linear
regression corresponds to an identity link function, constant variance and a
normal distribution. Logistic regression arises from a logit link function and
a binomial distribution (the variance of the response (npq) is related to its
mean (np): variance = mean (1 - (mean/n)). Loglinear models are used for
binomial or Poisson counts. Standard techniques for analyzing censored survival
data, such as the

**Genetic distance**: A measurement of genetic
relatedness of populations. The estimate is based on the number of allelic
substitutions per locus that have occurred during the separate evolution of two
populations. Link to a lecture on **Estimating
Genetic Distance** and **GeneDist:
Online Calculator of Genetic Distance**.
The software **Arlequin**, **PHYLIP**, **GDA**,
**PopGene**
and **SGS**
are suitable to calculate population-to-population genetic distance from allele
frequencies. See **Basic
Population Genetics**.

**Genetic
Distance Estimation by PHYLIP**: The most popular (and free) phylogenetics
program **PHYLIP** can be used to estimate genetic
distance between populations. Most components of PHYLIP can be run **online**. One component of the package **GENDIST** estimates genetic distance
from allele frequencies using one of the three methods: Nei's, Cavalli-Sforza's
or Reynold's (see papers by **Nei et al, 1983**, **Nei M, 1996** and a **lecture note** for more information on
these methods). GENDIST can be run **online** using the default options (**Nei's genetic distance**) to obtain
genetic distance matrix data. The PHYLIP program **CONTML** estimates phylogenies from
gene frequency data by maximum likelihood under a model in which all divergence
is due to genetic drift in the absence of new mutations (Cavalli-Sforza's
method) and draws a tree. The program comes as a freeware as part of PHYLIP or
this program can be run **online** with default options. If new
mutations are contributing to allele frequency changes, Nei's method should be
selected on GENDIST to estimate genetic distances first. Then a tree can be
obtained using one of the following components of PHYLIP: **NEIGHBOR** also draws a phylogenetic
tree using the genetic distance matrix data (from GENDIST). It uses either
Nei's "**Neighbor Joining Method**," or the
**UPGMA** (**u**nweighted **p**air **g**roup
**m**ethod with **a**rithmetic mean; average linkage clustering) method.
Neighbor Joining is a distance matrix method producing an unrooted tree without
the assumption of a clock (UPGMA does assume a clock). NEIGHBOR can be run **online**. Other components of PHYLIP
that draw phylogenetic trees from genetic distance matrix data are **FITCH** / **online** (does not assume evolutionary
clock) and **KITSCH** / **online** (assumes evolutionary clock).

**Geometric
mean**:
G = (*x*_{1}.*x*_{2}...*x*_{n})^{1/}^{n} where n is
the sample size. This can also be expressed as
antilog ((1/n) S log *x*), which means the
antilog of the mean of the logs of each value. See **Applications of the Geometric Mean; Spizman, 2008: Geometric Mean in Forensic Economy**.

**Greek letters**: It is customary to use Greek letters for
(usually unknown) population parameters and Roman letters/numerals for
estimated sample statistic(s). Note that most Greek letters used in
statistics/mathematics correspond to the initial of English word for the same
parameter (like **m**u (µ) and **m**ean; **s**igma (lowercase) (s) and **s**tandard
deviation; **S**igma (uppercase) (S) and **S**um; **P**i (P) and **P**roduct;
**e**psilon (€) and **e**rror etc). The first two Greek letters alpha (a) and beta
(b) are
reserved for type I (a) and type II (b) errors in statistics. See complete lists of statistical symbols
at link1; link2; link3; link4; link5.

**Half-normal
plot**:
A diagnostic test for model inadequacy or revealing the presence of outliers.
It compares the ordered residuals from the data to the expected values of
ordered observations from a normal distribution. While the full-normal plots
use the signed residuals, half-normal plots use the absolute values of the
residuals. Outliers appear at the top right of the plot as distinct points, and
departures from a straight line mean that the model is not satisfactory. It is
appropriate to use a half-normal plot only when the distribution is symmetrical
about zero because any information on symmetry will be lost.

**Haplotype
Relative Risk method**: This method uses non-inherited parental
haplotypes of affected persons as the control group and thus eliminates the
risks and bias associated with using unrelated individuals as controls in
case-control association studies, as well as the higher cost (see **Falk & Rubinstein, 1987**; **Knapp, 1993**; **Terwilliger & Ott, 1992**).

**Hardy-Weinberg equilibrium (HWE)**: In an infinitely large population, gene and genotype
frequencies remain stable as long as there is no selection, mutation, or
migration. For a bi-allelic locus where the gene frequencies are p and q: p^{2}+2pq+q^{2
}= 1 (see **Hardy-Weinberg parabola**). HWE should be
assessed in controls in a case-control study and any deviation from HWE should
alert for genotyping errors (**Lewis, 2002**) unless there are biological
reasons for any deviation (see **Ineichen & Batschelet, 1975** for the
effect of natural selection on Hardy-Weinberg equilibrium). (__ Online HWE Analysis__;

**Harmonic
mean**:
Of a set of numbers (y_{1} to y_{n}), the harmonic mean is the
reciprocal of the arithmetic mean of the reciprocal of the numbers [H = N /
(1/(y_{1} + y_{2} + .... y_{n}))]. The harmonic mean is
either smaller than or equal to the arithmetic mean. It is a measure of
position.

**Hazard
function**
(instantaneous failure rate, conditional failure, intensity, or force of
mortality function): The function that describes the probability of failure
during a very small time increment (assuming that no failures have occurred
prior to that time). Hazard is the slope of the survival curve – a measure of
how rapidly subjects are having the event (dying, developing an outcome etc).

**Hazard rate**: It is a time-to-failure function
used in
survival analysis. It is defined as the probability per time unit that a case
that has survived to the beginning of the respective interval will fail in that
interval. Specifically, it is computed as the number of failures per time units
in the respective interval, divided by the average number of surviving cases at
the mid-point of the interval.

**Hazard
ratio (relative hazard)**: Hazard ratio is an **effect size** which
compares two groups differing in exposures (including treatments), prognostic
variables or other (binary) characteristics in survival analysis.HR is the
relative risk/hazard, associated with an exposure, of suffering an event (like
relapse or death) in a fixed period of time. The response variable is a
survival time, and the coefficients of the (regression) model correspond to
logs of hazard ratios. If the hazard ratio is 2.0, then the rate of failure in
one group is twice the rate in the other group. The computation of the hazard
ratio assumes that the ratio (i.e., proportional hazard) is consistent over
time, and that any differences are due to random sampling. When performing any tests of hypotheses to compare survival curves,
the **proportionality of hazards assumption**
should be checked (and should hold for the validity of **Cox's proportional
hazard models**). (See also **Log-rank test**).

**Hetereoscedastic
data**:
Data that have non-constant (heterogeneous) variance across the predicted
values of y. In this case, residual graph will show varying variability across
the fitted values. This is a regression diagnostic problem and should be fixed
by transforming the data. See also **Homoscedasticity**.

**Heuristics**: A term in
computer science that refers to guesses made by a program to obtain
approximately accurate results. Frequently used in phylogenetics and
computational biology.

**Hierarchical
model**:
In linear modeling, models which always include all the lower-order
interactions and main effects corresponding to any interaction they include.

**Hierarchy
of strength of evidence**: The ranking of epidemiologic research designs
for the strength of the evidence they provide for causality. The highest
ranking design is the controlled interventional studies (prototype: **randomized
controlled/clinical trials/RCTs**). This design minimizes bias/confounding
(due to random allocation of subjects to comparative groups), reduces chance
effects due to large sample sizes and sufficient statistical power to begin
with, and eliminates reverse causation by virtue of being longitudinal studies
(and simply by mainly being drug trials). Thus, by minimizing alternative
explanations (chance/random variations; bias/confounding; reverse causality), randomized
controlled trials provide the strongest evidence for causality of the observed
relationship.

**Historical
fallacy**:
The mistake of assuming that an association observed in **cross-sectional data**
will be similar to that observed in longitudinal data or vice versa.

**Homoscedasticity
(homogeneity of variance):** Normal-theory-based tests for the equality of
population means such as the t-test and analysis of variance, assume that the
data come from populations that have the same variance, even if the test
rejects the null hypothesis of equality of population means. If this assumption
of **homogeneity of variance** is not met, the statistical test results may
not be valid. **Heteroscedasticity** refers to lack of homogeneity of
variances.

**Hotelling's
T ^{2} test**: This is a generalization of Student's t-test
for multivariate data. Designed to provide a global significance test for the
difference between two groups with simultaneously measured multiple
dependent/outcome variables and multiple explanatory/independent variables. It
can also be used for one group with simultaneously measured multiple dependent
outcome variables (another test similar to Hotelling's T

**Hypergeometric
distribution**: A probability distribution of a discrete variable
generally associated with sampling from a finite population without replacement.
An example may be that given a lot with 25 good units and five faulty. The
probability that a sample of five will yield not more than one faulty item
follows a hypergeometric distribution.

**Index
plot**:
An index plot plots each **residual**, **leverage**, or **Cook's
distance** against the corresponding observation (row) number (*i* or
index) in the dataset. In many cases, the row number corresponds to the order
in which the data were collected. If this is the case, this would be similar to
plotting the residuals (or another diagnostic quantity) against time. The index
plot is a helpful diagnostic test for normal linear and particularly
generalized linear models. Both outliers and influential points can be detected
by the index plot. It is particularly useful when the data is in time order so
that pattern in the residuals, etc. over time can be detected. If a residual
index plot is showing a trend in time, then they are not independent (violation
of a major assumption of linear regression).

**Inferential
statistics**:
Making inferences about the population from which a sample has been drawn and
analyzed.

**Influential
points**:
Observations that actually dominate a **regression analysis** (due to high **leverage**,
high **residuals** or their combination). The method of ordinary **least
squares** gives equal weight to every observation. However, every observation
does not have equal impact on the **least squares** results. The slope, for
example, is influenced most by the observations having values of the
independent variable farthest from the mean. An observation is influential if
deleting it from the dataset would lead to a substantial change in the fit of
the **generalized linear model**. High-leverage points have the potential to
dominate a regression analysis but not necessarily exert an influence (i.e., a
point may have high leverage but low influence as measured by **Cook
statistics**). Cook statistics is used to determine the influence of a data
point on the model.

**Interaction**: If the
effect of one factor depends on the level of another factor, the two factors
involved are said to interact, and a contrast involving all these levels is
called their interaction. Factors A and B interact if the effect of factor A is
not independent of the level of factor B. For example, when there are two main effects
on a response variable, if their combined effect is higher than the sum of
their main effects due to a bonus (say, the effects of a kind of exercise and a
kind of diet on blood lipid levels), they have an interaction (meaning a simple
additive model is not sufficient to account for the observed data and a
multiplicative term must be added). Briefly, interaction is a deviation from
additivity. Also, there would be an interaction between the factors sex and
treatment if the effect of treatment was not the same for males and females in
a drug trial. Interaction is closely linked with **effect modification** in
epidemiology (see **Genetic Epidemiology Glossary**; **Wikipedia**: **Statistical Interaction**).

**Intercept**: In linear
regression, the intercept is the mean value of the response variable when the
explanatory variable takes the value of zero (the value of y when x=0).

**Interpolation**: Making
deductions from a model for values that lie between data points. Making
deductions for values beyond the data points is called extrapolation and the
results are not valid.

**Interquartile
range (dQ)**:
dQ is a measure of spread and is the counterpart of the standard deviation for
skewed distributions. dQ is the distance between the upper and lower quartiles
(Q_{U}-Q_{L}).

**Interval
variable**
(equivalent to **continuous variable**): A quantitative variable measured on
a scale with constant intervals (like days, milliliters, kilograms, miles so
that equal-sized differences on different parts of the scale are equivalent)
where the zero point and unit of measurement are arbitrary. When temperature is
measured on two scales, Fahrenheit and Centigrade, the zero points in these two
scales do not correspond, and a 10% increase in Fahrenheit (from 50^{0}
to 55^{0}) is not a 10% increase in the corresponding Centigrade scale
(10^{o} to 12.8^{o} = 2.8%); these two measurements cannot be
mixed or compared. For estimation of correlation coefficients, data should be
interval type (See also **ratio variable **and **variable**).

**Kolmogorov-Smirnov
two-sample test**: A non-parametric test applicable to continuous frequency
distributions. It is considered to be the equivalent of the C^{2}-test for
quantitative data and has greater power than the G-statistics or C^{2}-test for
goodness of fit especially when the sample size is small. It can be used to
compare two independent groups. The test is based on differences between two
cumulative relative frequency distributions (it compares the distributions not
the parameters). Thus, the **Kolmogorov-Smirnov** test is also sensitive to
differences in the general shapes of the distributions in the two samples such
as differences in dispersion, skewness. Its interpretation is similar to that of the Wald-Wolfowitz runs test. **Online
Kolmogorov-Smirnov “One-Sample” Test** at **Vassar**.

**Kruskal-Wallis test **(One-way ANOVA by ranks): It
is one of the non-parametric tests equivalent to one-way **ANOVA** that are
used to compare *multiple* (k > 2) *independent* samples. This
test assesses the hypothesis that the different samples in the comparison were
drawn from the same distribution or from distributions with the same median. It
can be used to analyze ordinal variables. It is an extension of the **Mann-Whitney
(U) test **(for two independent samples). The interpretation of the
Kruskal-Wallis test is identical to that of one-way ANOVA, but is based on
ranks rather than means.

**Kurtosis**: Kurtosis is a measure
of whether the data are peaked or flat in its distribution relative to a normal
distribution (whose kurtosis is zero). Positive kurtosis indicates a ‘peaked’
distribution and negative kurtosis indicates a ‘flat’ distribution (data sets
with high kurtosis have a distinct peak near the mean and decline rapidly; data
sets with low kurtosis tend to have a flat top near the mean rather than a
sharp peak) (**Definition of Kurtosis and Skewness**;** Online Skewness-Kurtosis Calculator**). See also **skewness**.

**Large sample effect**: In large samples, even
small or trivial differences can become statistically significant. This should be
distinguished from biological/clinical importance.

**Least
squares method**: A method of fitting a straight line or curve based one
minimization of the sum of squared differences (residuals) between the
predicted and the observed points. Given the data points (x_{i}, y_{i}), it is
possible to fit a straight line using a formula, which gives the y=a+bx. The
gradient of the straight line b is given by [S(x_{i} - m_{x})(y_{i}-m_{y})] / [(S(x-m_{x}))^{2}], where m_{x} and m_{y} are the
means for x_{i} and y_{i}. The
intercept a is obtained by m_{y} - bm_{x}. See **Wikipedia**: **Least Squares**.

**Leverage
points**:**
**In regression analysis, these are the observations that have an extreme
value on one or more explanatory variable. The leverage values indicate whether
or not X values for a given observation are outlying (far from the main body of
the data). A high leverage value indicates that the particular observation is
distant from the center of the X observations. High-leverage points have the
potential to dominate a regression analysis but not necessarily influential. If
the residual of the same data point and **Cook's distance** are also high,
then it is an influential point. See also **influential points**.

**Likelihood**: The
probability of a set of observations given the value of some parameter or set
of parameters. For example, the likelihood of a random sample of n observations
(x_{1} to x_{n}) with probability distribution f(x; q ) is given
by: L = P f(x_{i};
q _{0}).
This function, which applies equally to continuous density and discrete mass
functions, is the basis of maximum likelihood estimation.

**Likelihood
distance test (likelihood residual, deletion residual)**: This test
is based on the change in deviance when one observation is excluded from the
dataset. It uses the difference between the log-likelihood of the complete
dataset and the log-likelihood when a particular observation is removed. A
relatively large difference indicates that the observation involved is an
outlier (poorly fitted by the model).

**Likelihood
ratio test**:
A general purpose test of hypothesis H_{o} against an alternative H_{1}
based on the ratio of two likelihood functions one derived from each of H_{o}
and H_{1.} The statistics l is given by l = -2 *ln* (L_{H0
}/ L_{H1}) has approximately a C^{2}
distribution with df equal to the difference in the number
of parameters in the two hypotheses. One application of this test is the **G-test**,
which is used in categorical data analysis as a goodness-of-fit or independence
test (the tests statistics has a Chi-squared distribution).

**Linear expression**: A **polynomial**
expression with the degree of **polynomial** being 1. It will be something
like, f(x)=2x^{1}+3, but not
x^{2}+2x+4.

**Linear
logistic model**: A linear logistic model assumes that for each possible
set of values for the independent (X) variables, there is a probability p that
an event (success) occurs. Then the model is that Y is a linear combination of
the values of the X variables: Y = b_{o} + b_{1}*X_{1} + b_{2}*X_{2} + b_{3}*X_{3} + … b_{k}*X_{k}, where Y is
the logit transformation of the probability p. Logistic in statistical usage
stems from logit and has nothing to do with the military use of the word which
means the provision of material.

**Linear
regression models**: In the context of linear statistical modeling,
'linear' means linear in the parameters (coefficients), not the explanatory
variables. The explanatory variables can be transformed (say, x^{2}),
but the model will still be linear if the coefficients remain linear. When the
overall function (Y) remains a sum of terms that are each an X variable
multiplied by a coefficient, the function Y is said to be linear in the coefficients. A non-linear model is different in that it
has a non-constant slope (a tutorial on **Simple
Linear Regression**; see also **Vassar College**;
Excel macro for **Linear Correlation & Regression**).

**Linkage
disequilibrium**: Also called gametic association, which is more
appropriate. It means increased probability for two or more alleles to be on
the same chromosome at the population level. In a population at equilibrium,
haplotype frequency is obtained by multiplying the allele
frequencies x2. When there is linkage disequilibrium, the observed frequency
(say by family analysis or sperm typing) is different from the expected
frequency. The difference gives the D
value (D for difference), which can be tested for significant difference from 0
by a 2x2 table analysis (for two alleles). The D
value can be negative or positive. Linkage disequilibrium can derive from
population admixture, tight linkage or elapse of insufficient time for the
population to reach equilibrium. A classic example in immunogenetics is the
HLA-A1-B8-DR3 haplotype which shows significant linkage disequilibrium
extending over 6.5 Mb. Software for LD estimation: **Genetic Data Analysis**,
**EH**,** ****2LD****, MLD, **

**Link
function**:
A particular function of the expected value of the response variable used in
modeling as a linear combination of the explanatory variables. For example,
logistic regression uses a *logit* link function rather than the raw
expected values of the response variable; and Poisson regression uses the *log*
link function. The response variable values are predicted from a linear
combination of explanatory variables, which are connected to the response
variable via one of these link functions. In the case of the general linear
model for a single response variable (a special case of the generalized linear
model), the response variable has a normal distribution and the link function
is a simple identity function (i.e., the linear combination of values for the
predictor variable(s) is not transformed).

**LOD
Score**:
Stands for the logarithm of odds. It is a statistical measure of the likelihood
that two genetic markers occur together on the same chromosome and are
inherited as a single unit of DNA (co-segregation). The LOD score serves as a
test of the null hypothesis of free recombination versus the alternative
hypothesis of linkage. Determination of LOD scores requires pedigree analysis
and a score of >3 is traditionally taken as evidence for linkage. Linkage is
between two genetic loci but not alleles. An example is the linkage between the
hemochromatosis gene (HFE) and HLA-A. This means that within the same family
all affected subjects will have the same HLA-A allele but not necessarily a
particular one, i.e., there will be no recombination between HFE and HLA-A. LOD
score has nothing to do with **linkage disequilibrium**.

**Log
0**
is undefined. If we need to use a log transformation but some data values are
0, the usual way to get round this problem is to add a small positive quantity
(such as 1/2) to all the values before taking logs.

**Log-rank
test (of equality across strata)**: A non-parametric test of significance
for the difference between two survival curves (for categorical data). It is a
special application of the **Mantel-Haenszel test**. It can be adjusted for
confounders (not preferable to **Cox proportional hazard regression** which
is a semi-parametric model), or performed for trend (for details and parametric
alternatives, see **Lee & Go, 1997**). It was developed by Mantel and Haenszel as an
adaptation of the **Mantel-Haenszel ****C**^{2}** test**. The other commonly used nonparametric
tests for comparison of two survival distributions are the Gehan's Generalized
Wilcoxon test (**Gehan** **1965a** & **1965b**). The
log-rank test is appropriate for survival distributions whose hazard functions
are proportional over time, i.e. the two survival curves do not cross (**proportionality assumption**). Otherwise, the Gehan's Wilcoxon test is recommended. The
Wilcoxon statistic puts more weight on early deaths compared to the log-rank (**Lee & Go, 1997**).
See **UCLA Stata**:** Survival Analysis & Log-rank Test**; **BMJ Statistics Notes**: **Logrank Test**.

**Log
transformation**: This transformation pulls smaller data values apart and
brings the larger data values together and closer to the smaller data values
(shrinkage effect). Thus, it is mostly used to shrink highly positively skewed
data.

**Logistic
(binary) regression**: A statistical analysis most frequently models
the relationship between a dichotomous (binary) outcome variable (such as
diseased or healthy; dead or alive; relapsed or not relapsed), and a set of
explanatory variables of any kind (such as age, HLA type, blood pressure, kind
of treatment, disease stage etc). It can also be used when the outcome variable
is polytomous (several categories of the prognosis; including ordinal response
'ordinal logistic regression' or 'proportional odds ratio model'), and when
there are several outcome variables (multinomial logistic regression - a
special class of loglinear models). Analysis of data from case-control studies
via logistic regression can proceed in the same way
as cohort studies. See **Logistic Regression Lecture Note**, **Online
Logistic Regression**,** Logistic Regression
with SPSS,** **STATA** and **SAS**; **Power Calculation for Logistic Regression (including
Interaction)**.

**Logit
transformation**: The logit (or logistic) transformation Y of a probability
p of an event is the logarithm of the ratio between the probability that the
event occurs (p) and that the event does not occur (1-p): Y = ln (p/(1-p)).
Thus it is a transformation of a binary (dichotomous) response variable. The
logit transformation of p is also known as the *log odds* of p, since it
is the logarithm of the **odds**. There are other link functions for binary
response variables.

**Loglinear
model**:
Multinomial data (from contingency tables) can be fitted by using a generalized
linear model with a Poisson response distribution and a log link function. The
resulting models for counts in the cells of a contingency table are known as
loglinear models in which the logarithm of the expected value of a count
variable is modeled as a linear function of parameters. Loglinear models try to
model all important associations among all variables. In this respect, they are
related to **ANOVA** models for quantitative data. Loglinear modeling allows
more than two discrete variables to be analyzed and interactions between any
combination of them to be identified. Associations between variables in
log-linear models are analogous to interactions in **ANOVA** models. The aim
is to find a small model, which achieves a reasonable fit (small residual).
Data sets with a binary response (outcome) variable and a set of explanatory
variables that are all categorical can be modeled either by **logistic
regression** or by loglinear modeling. More on **Loglinear Models **and **Online
Loglinear Test** at **Vassar College**.

In
loglinear modeling, the counts in the cells of the contingency table are
treated as values of the response variable rather than either of the
categorical variables defining the rows and columns. A loglinear model relates
the distribution of the counts to the values of the explanatory variables (rows
and columns), and tests for the presence of an interaction between them.
Whether the interaction term is necessary is tested by fitting two models, one
without and one with the interaction term, and using the difference between the
regression deviance values for the two models (as well as the difference in
df). If the SP obtained by the C^{2}-test is small, the
interaction term should be in the model. This corresponds to a significant
difference in Fisher's exact test or the C^{2}-test. For a
2x2 table, the difference between the regression deviances in the two models is
the same as the residual deviance in the model with no interaction term. In
loglinear modeling, it makes no sense if any main effect is omitted.

In loglinear modeling (as in other GLMs), the saturated model, which includes all interactions, fits the data exactly, and the fitted values are exactly equal to the observed values (the residual deviance is zero). The residual deviance of any other model can be used to test how worse it is compared to the saturated model. A small SP arising from a high residual deviance would mean that it does not fit better than the saturated model (the alternative model is rejected). If the associated SP is not small, then the model is not significantly worse than the saturated model (i.e., the exact fit).

An important constraint on the choice of models to fit, i.e., which terms and which interactions to include, is that whether any marginal totals (row or column) for combinations of terms are fixed in advance. If this applies to any combination of terms, their interaction must be included (the main effects and this particular interaction make up the null model for such data). When only the overall sample size is fixed in advance, there is no constraint on the models that can be fit.

When there are more than two variables, whether any interaction can be omitted from the model can be tested by fitting a model containing all the interactions of a particular order (say, second-order interactions). Then, new models omitting each interaction are fitted. Each new model's regression deviance is compared with the regression deviance of the model containing all interactions. If the resulting difference in regression deviances (and df) results in a large SP, that interaction can be omitted (it is not significantly different but has fewer terms; or it does not significantly contribute to the model).

The loglinear model can be used to find a conditional probability involving the factors in the contingency table for one of the factors chosen as a response variable. When the factor chosen as a response variable is binomial, logistic regression can be used to analyze the same data (a binomial response variable and categorical explanatory variables). Logistic regression substitutes the loglinear model with equal results as long as the fitted loglinear model includes the main effects of the response variable and a saturated model for all the explanatory variables.

**Longitudinal
data**:
Data collected over a period of time as in cohort studies. These data are
usually analyzed by using **survival analysis** techniques. See **Longitudinal Studies Chapter** in **Epidemiology for the Uninitiated**.

**Lurking variable**: See **confounding variable**.

**Mann-Whitney
(U) test**:
A non-parametric test for comparing the distribution of a continuous variable
between two *independent* groups. It is analogous to the independent
two-sample t-test, so that it can be used when the data are not normally
distributed. The Wilcoxon signed ranks T-test for independent samples is
another non-parametric alternative to the t-test in
this context (for *paired* samples, **Wilcoxon matched pairs signed rank
test** should be used) (online **Mann-Whitney
Test**). See also **Non-parametric tests on two independent
samples in XLStat
and IBM-SPSS**.

**Mantel-Haenszel ****C**^{2}** test** (also
called Cochran-Mantel-Haenszel (CMH) test): Test for a null hypothesis of no overall
relationship in a series of 2x2 tables for stratified data derived either from
a cohort or a case-control study. It allows analysis of **confounding** and
gives an **adjusted odds ratio** or relative risk. It can be used on
categorical or categorized continuous data. The test is only valid when the
variance of observed data is ³ 5. It is inappropriate when the association
changes dramatically across strata (heterogeneity is usually tested by
Breslow-Day test). It is, however, applicable for sparse data sets for which
asymptotic theory does not hold for G^{2}. The test statistics, M^{2},
has approximately a Chi-squared distribution with df = 1 (see a review by **Stefano & Ezio, 2007**; online **Mantel-Haenszel Test** for a single table). Mantel & Haenszel’s 1959 JNCI
paper is now a **citation classic**.

**Markov Chain Monte Carlo** (**MCMC,
random walk Monte Carlo) methods**: See **Wikipedia**: **MCMC**; **MCMC Applet**;
**Markov Chain Simulation Applets**; **Buffon's
Needle Applet**; **Monte
Carlo Methods Links**; **MCMC Tests of Genetic Equilibrium (Lazzeroni & Lange,
1997)**; **Markov Chain Monte Carlo in Practice (Book) **and**
Virtual Labs:
Markov Chain** (requires **MathPlayer**).
See also **Metropolis-Hastings algorithms**.

**Maximum
likelihood**:
This method is a general method of finding estimated values of parameters. It
yields values for the unknown parameters, which maximize the probability of
obtaining the observed values. The estimation process involves considering the
observed data values as constants and the parameter to be estimated as a
variable, and then using differentiation to find the value of the parameter
that maximizes the likelihood function. First a likelihood function is set up
which expresses the probability of the observed data as a function of the
unknown parameters. The maximum likelihood estimators of these parameters are
chosen to be those values, which maximize this function. The resulting
estimators are those, which agree most closely with the observed data. This
method works best for large samples, where it tends to produce estimators with
the smallest possible variance. The maximum likelihood estimators are often biased in small samples (see **maximum
likelihood**). Another method for point
estimation is the **method of
moments**.

**McNemar's
test**:
A special form of the Chi-squared test used in the analysis of paired (not
independent) proportions. This non-parametric test compares two correlated
dichotomous responses and finds its most frequent use in situations where the
same sample is used to find out the agreement (concordance) of two diagnostic
tests or difference (discordance) between two treatments. If the pairs of data
points are the measurements on two matched people (such as affected and
unaffected siblings) in a case-control study, or two measurements on the same
person, the appropriate test for equality of proportions is the McNemar's test.
It can be used to assess the outcome of two treatments applied to the same
individuals or the significance of the agreement between two detection methods
of a physical sign. If there are more than two periods of
data collection (such as pretest, posttest and follow-up), **Cochran's Q test**
should be used (**Online
McNemar's Test** **(1)
(2) (3)**.** **

**Mean** (or
average): A measure of location for a batch of data values; the sum of all data
values divided by the number of elements in the distribution. Its accompanying
measure of spread is usually the **standard deviation**. Unlike the **median**
and the **mode**, it is not appropriate to use the
mean to characterize a skewed distribution (see also **standard deviation**)
(**Online Calculator for Mean**).

**Mean
squares**:
A sum of squares divided by its associated df is a mean square. In **ANOVA**,
the regression (explained) mean square is **ESS**/k-1, and the residual
(error) mean square is **RSS**/N-k. Note that the mean squares are not
additive, i.e., they do not add up to **TSS**/N-1. Importantly, the residual
(error) mean square is an unbiased estimator of the variance (s^{2}) in ANOVA.
The regression (explained) mean square equals to the variance only when the
slope (b) is zero. Their ratio (mean square ratio = regression MS / residual
MS) is therefore provides a test for the null hypothesis that b = 0. Large F
values support the alternative hypothesis that the slope is not zero. This is
the basis of the **F test** in **ANOVA**.

**Measurement
type****:**** **The data may
be measured in the following scales: nominal, ordinal, interval or ratio scales
(known as Stevens' typology). The scale of the measurements may be (other than
nominal scale measurements) either continuous or discrete, and either bounded
or unbounded.

**Measures of association**: These
measures include the **Phi coefficient of association**, **Cramer's
contingency coefficient (C) and V**, Kendall's tau-b and (Stuart's) tau-c,
Somers' D (a modification of Kendall's tau-b), Yule's Q, gamma, Spearman's rank
correlation coefficient (rho), Pearson's correlation coefficient, lambda
(symmetric and asymmetric), uncertainty coefficients (symmetric and
asymmetric), Guttman's coefficient of predictability (lambda)/Goodman-Kruskal's
lambda, Goodman-Kruskal's gamma and Goodman-Kruskal's tau (concentration
coefficient). See a review on Measures of Association by **Jaeschke, 1995**; **Measures of Effect-Size & Association**; **Measures of Association for Cross-tabulations**;
Measures on **SAS**, **STATA**, **SYSTAT**;
**Ennis, 2001**; **Morton,
2001**.

**Measures
of central tendency**: These are parameters that characterize an
entire distribution. These include **mode**, **median** and **mean**.

**Measures
of variability**: These are parameters that characterize an scatter of a
distribution. These include **range (including interquartile range)**, **standard
deviation **and **variance**.

**Median**: Another
measure of location just like the **mean**. The value that divides the
frequency distribution in half when all data values are listed in order. It is
insensitive to small numbers of extreme scores in a distribution. Therefore, it
is the preferred measure of central tendency for a skewed distribution (in
which the mean would be biased) and is usually paired with the **interquartile
range (dQ)** as the accompanying measure of spread. See Martin Bland's page
for calculation of **confidence intervals for a median**.

**Median
absolute deviation (MAD) or absolute deviation around the mean**: This is a robust
measure of central tendency insensitive to outliers. The MAD is the median of
the absolute values of the residuals (deviations) from the data’s median. MAD
can easily be calculated **using R**. See
the article on **MAD in R-Bloggers**.

**Median
test**:
This is a crude version of the **Kruskal-Wallis ANOVA **in that it assesses
the difference in samples in terms of a contingency table. The number of cases
in each sample that fall above or below the common median is counted and the
Chi-square value for the resulting 2xk samples contingency table is calculated.
Under the null hypothesis (all samples come from populations with identical
medians), approximately 50% of all cases in each sample are expected to fall
above (or below) the common median. The median test is particularly useful when
the scale contains artificial limits, and many cases fall at either extreme of
the scale. In this case, the median test is the most appropriate method for comparing samples (**Online Median Test**).

**Meta-Analysis**: A systematic approach yielding an
overall answer by analyzing a set of studies that address a related question.
This approach is best suited to questions, which remain unanswered after a
series of studies. Meta-Analysis provides a weighted average of the measure of
effect (such as odds ratio). The rationale is to increase the power by
analyzing the sets of data. The selection of studies to include in a
meta-analysis study is the main problem with this approach. **Funnel Plot** is an informal method to assess
the effect of publication bias in this context. See also **Introduction to Meta-Analysis** by the
Cochrane Collaboration; **Meta-Analysis in Epidemiology** by Stroup et
al (2000); **Methods for Meta-Analysis in Medical Research**** **by AJ
Sutton; **Introduction to Meta-Analysis** by
Borenstein et al (2009), **Online Meta-Analysis Tests**, **Meta-Analysis presentation by MT Dorak**.

**Metropolis-Hastings
algorithms**:
These algorithms are a class of Markov chains which are commonly used to
perform large scale calculations and simulations in Physics and Statistics. See
**Metropolis-Hastings Applet**. See **Markov
Chain Monte Carlo methods**.

**Mode**: The observed value that occurs
with the greatest frequency. The mode is *not* influenced by small numbers
of extreme values.

**Model building**: The traditional approach to
statistical model building is to find the most parsimonious model that still
explains the data. The more variables included in a model (overfitting), the
more likely it becomes mathematically unstable, the greater the estimated
standard errors become, and the more dependent the model becomes on the
observed data. Choosing the most adequate and minimal number of explanatory
variables helps to find out the main sources of influence on the response
variable, and increases the predictive ability of the model. Ideally, there
should be more than 10 observations for each variable in the model. The usual
procedures used in variable selection in regression analysis are: univariate
analysis of each variable (using C^{2}
test), stepwise method (backward or forward elimination of variables; using the
deviance difference), and best subsets selection. Once the essential main
effects are chosen, interactions should be considered next. As in all model
building situations in biostatistics, biological considerations should play a
role in variable selection.

**Monte Carlo trial**: Studying a complex
relationship difficult to solve by mathematical analysis by means of computer
simulations. An online book on **Resampling
Statistics**, and software (**CLUMP**, downloadable from **clump22.zip**) to do Monte Carlo statistics
for case-control association studies.

**Multicolinearity**: In
multiple regression, two or more X variables are colinear if they show strong linear
relationships. This makes estimation of regression coefficients impossible. It
can also produce unexpectedly large estimated standard errors for the
coefficients of the X variables involved. This is why an exploratory analysis
of the data should be first done to see if any collinearity among explanatory
variables exists. Multicolinearity is suggested by non-significant results in
individual tests on the regression coefficients for important explanatory
(predictor) variables. Multicolinearity may make the determination of the main
predictor variable having an effect on the outcome difficult.

**Multiple regression**: To quantify the
relationship between several independent (explanatory) variables and a
dependent (outcome) variable. The coefficients (a, b_{1} to b_{i}) are
estimated by the **least squares** method, which is equivalent to **maximum
likelihood** estimation. A multiple regression model is built upon three
major assumptions:

1. The response variable is normally distributed,

2. The residual variance does not vary for small and large fitted values (constant variance),

3. The observations (explanatory variables) are independent.

Multiple regression is
the prototype for **general linear models** because the response variable
should be normally distributed and there is no **link function**, whereas,
simple linear regression is a special case for **generalized linear models**.
The extension of multiple regression to multivariate data analysis is called
canonical correlation (**Online Multiple
Regression**; **Reference Guide on Multiple Regression**).

**Multiple
regression correlation coefficient (R ^{2} - R-squared)**: R

**Multiplication
rule**:
The probability of two or more statistically independent events all occurring
is equal to the product of their individual probabilities. If the probability
of having trisomy 21 is *a*, and trisomy 6 is *b*, assuming
independence of these two events, for a baby the probability of having both
trisomies is (*a* x *b*). One of the most critical errors of judgment
in the use of independence assumption relates to a court case in the UK (**Watkins, 2000**). (See also **addition
rule**.)

**Multivariable
analysis**:
As opposed to univariable analysis, statistical analysis performed in the
presence of more than one explanatory variable to determine the relative
contributions of each to a single event is (or should be) called multivariable
analysis (in practice, however, it is called univariate and multivariate
analysis more frequently). It is a method to simultaneously assess
contributions of multiple variables or adjust for the effects of confounders.
Multiple linear regression, multiple logistic regression, proportional hazards
analysis are examples of multivariable analysis, which has no similarity whatsoever to **multivariate analysis** (see
also **Peter TJ, 2009**). See a review on **Multivariable Methods by MH Katz**
(book on **Multivariable Analysis by MH Katz**).

**Multivariate
analysis**:
Methods to deal with more than one related 'outcome/dependent variable' (like
two outcome measures from the same individual) simultaneously with adjustment
for multiple confounding variables (covariates). When there is more than one
dependent variable, it is inappropriate to do a series of univariate tests. **Hotelling's
T ^{2}** test is used when there are two groups (like cases and
controls) with multiple dependent measures (may be more than two), and
multivariate analysis of variance (

**Multivariate analysis of variance (MANOVA)**: An
extension of **Hotelling's T ^{2}** test to more than two groups with
related 'multiple' outcome measures. Groups are compared on all variables
simultaneously as a global test (rather than one-by-one as ANOVA does). See
also

**Natural
(raw) residuals**: The difference between the observed (Y* _{i}*)
and fitted values (Y

**Negative
predictive value**: Probability of a true negative as in a person
identified healthy by a test is really free from the disease (see also **positive
predictive value)**.

**Nested
model**:
Models that are related where one model is an extension of the other.

**Nominal
variable**:
A qualitative variable defined by mutually exclusive unordered categories such
as blood groups, races, sex etc. (see also **ordinal variable**).

**Nonlinear
Regression**:
Regression analysis in which the fitted (predicted) value of the response
variable is a nonlinear function of one or more X variables. **GraphPad Guide to Nonlinear Regression**, **Introduction to Nonlinear Regression**,
**A GraphPad Practical Guide to Curve Fitting**.

**Nonparametric
methods**
(distribution free methods): Statistical methods to analyze data from
populations, which do not assume a particular population distribution. **Mann-Whitney
U test**, **Kruskal-Wallis test** and **Wilcoxon's (T) test** are
examples. Such tests do not assume a distribution of the data specified by
certain parameters (such as mean or variance). For example, one of the
assumptions of the Student's t-test and ANOVA is normal distribution of the
data. If this is not valid, a non-parametric equivalent must be used. If a
wrong choice of test has been made, it does not matter very much if the sample
size is large (a non-parametric test can be used where a parametric test might
have been used but a parametric test can only be used when the assumptions are
met). For a small sample size, non-parametric tests tend to give a larger *P*
value. In general, parametric tests are more robust, more complicated to compute and have greater power efficiency. Parametric
tests compare parameters such as the mean in t-test and variance in F-test as
opposed to non-parametric tests that compare distributions. Nonparametric
methods are most appropriate when the sample sizes are small. In large (e.g., *n*
> 100) data sets, it makes little sense to use nonparametric statistics (reviews
of non-parametric tests by **Whitley & Ball, 2002** and **Bewick, 2004**; a tutorial on **Parametric
vs Nonparametric Methods**; **Review
of Nonparametric Tests** in **Intuitive
Biostatistics; Nonparametric Tests for Ordinal Data **at** Vassar**).

**Normal distribution** (Gaussian distribution) is
a model for values on a continuous scale. A normal distribution can be
completely described by two parameters: mean (m)
and variance (s^{2}). It is
shown as C ~ N(m, s^{2}).
The distribution is symmetrical with mean, mode, and median all equal at m.
In the special case of m = 1 and s^{2}
= 1, it is called the standard normal distribution. See **Normal Distribution (1)**,
**(2)**,** (3) **& **(4)**.

**Normal
probability plot of the residuals**: A diagnostic test for the assumption of
normal distribution of residuals in linear regression models. Each residual is
plotted against its expected value under normality. A plot that is nearly
linear suggests normal distribution of the residuals. A plot that obviously
departs from linearity suggests that the error distribution is not normal.

**Null
Hypothesis Statistical Testing** (NHST): The most common method of
inferential statistical by which an observation is tested against a (null)
hypothesis of no effect or no relationship. The opposing hypothesis is the **alternative
hypothesis**.

**Null
model**:
A model in which all parameters except the intercept are 0. It is also called
the intercept-only model. The null model in linear regression is that the slope
is 0, so that the predicted value of Y is the mean of Y for all values of X.
The F test for the linear regression tests whether the slope is significantly
different from 0, which is equivalent to testing whether the fit using non-zero
slope is significantly better than the null model with 0 slope.

**Number needed to treat**: The
reciprocal of the reduction in absolute risk between treated and control groups
in a clinical trial. It is interpreted as the number of patients who need to be
treated to prevent one adverse event. See also **Number Needed to Treat (NNT) Guide by Bandolier (1)
(2)**; **Interpreting
Diagnostic Tests;** **NNT Calculator**; **GraphPad
NNT Calculator**;** EpiMax Table Calculator**;** Evidence-based
Medicine Toolbox** and articles
by **Cook & Sackett, 1995**; **Wu, 2002**; **Barratt,
2004**.

**Observational study**: An epidemiologic
study design in which subjects select themselves into groups (such as cases and
controls) and the investigator is a passive observer (no intervention). An
observational study may be descriptive or analytic. In an observational
study, causality of the observed associations cannot be established, and
residual confounding cannot be entirely ruled out. Bias may also play a role in
generating a spurious correlation or deviating the results. Cohort studies are
the highest-ranking observational study type in the hierarchy of causality
pyramid (but still below interventional studies).

**Odds**: The odds
of a success is defined as the ratio of the probability of a success to the probability
of a failure (p/(1-p)). If a team has a probability of 0.6 of winning the
championship, the odds for winning is 0.6/(1-0.6) = 3:2. Similarly, the odd in
a case-control study is the frequency of the presence of the marker divided by
the frequency of absence of the marker (in cases or controls separately). The
link function logit (or logistic) is the log_{e} of the odds.

**Odds
multiplier**:
In logistic regression, b = log_{e} (odds ratio), thus exp b = odds
ratio. For a continuous (explanatory) variable, exp b is called the odds
multiplier and corresponds to the odds ratio for a unit increase in the
explanatory variable. The odds multiplier of the coefficient is the odds ratio
for its level relative to the reference level. If x increases from a to b by c,
the odds multiplier becomes exp (cb). The resulting value shows the proportional
change in the odds associated with x = b relative to x = a. It follows that for
binary variables where x can only get values of 0 and 1, exp b = odds
ratio.

**Odds
ratio (OR)**:
Also known as relative odds and approximate relative risk. It is the ratio of
the odds of the risk factor in a diseased group and in a non-diseased (control)
group (the ratio of the frequency of presence / absence of the marker in cases
to the frequency of presence / absence of the marker in controls). The
interpretation of the OR is that the risk factor increases the odds of the
disease ‘OR’ times. OR is used in retrospective case-control studies (**relative
risk** (RR) is the ratio of proportions in two groups which can be estimated
in a prospective -cohort- study). These two and **relative hazard** (or **hazard
ratio**) are measures of the strength/magnitude of an association. As opposed
to the *P* value, these do not change with the sample size. OR and RR are
considered interchangeable when certain assumptions are met, especially for
large samples and rare diseases. Odds ratio is calculated as ad/bc where
a,b,c,d are the entries in a 2x2 contingency table (hence the alternative
definition as the cross-product ratio). In logistic regression, the coefficient
b corresponds
to the log_{e} of the odds ratio. There are
statistical methods to test the homogeneity of odds ratios (**Online Odds-Ratio
& 95% CI Calculation**; **Odds
Ratio-Relative Risk Calculation **(**Calculator
3**) in **Clinical Research Calculators** at **Vassar**).

**Offset**: A fixed,
already known regression coefficient included in a **generalized linear model**
(which does not have to be estimated).

**Omnibus test**: If the
chi-square test has more than one degree of freedom (larger than 2x2 table), it
is called an ‘omnibus’ test, which evaluates the significance of an overall
hypothesis containing multiple sub-hypotheses (these multiple sub-hypotheses
then need to be tested using follow up tests).

**One-way
ANOVA**:
A comparison of several groups of observations, all of which are independent
and subjected to different levels of a single treatment (such as cells exposed
to different dosage of a growth factor). It may be that different groups were
exposed to the same treatment (different cell types exposed to a new agent).
The main interest focuses on the differences among the means of groups.

**Ordinal
variable**:
An ordered (ranked) **qualitative/categorical** variable. The degree of HLA
matching (one, two, three or four antigen matching in two loci) in transplant
pairs, or HLA sharing in parents (one-to-four shared antigens) are ordinal
variables although the increments do not have to be equal in magnitude (see **interval**
and **ratio variables**). An ordinal variable may be a categorized
quantitative variable. When two groups are compared for an ordinal variable, it
is inappropriate to use ordinary Chi-squared test but the **trend test** or
its equivalents must be used.

**Outcome
(response, dependent) variable**: The observed variable, which is shown
on y axis. A statistical model shows this as a function of predictor
variable(s).

**Outlier**: An extreme
observations that is well separated from the remainder of the data. In regression
analysis, not all outlying values will have an influence on the fitted
function. Those outlying with regard to their X values (high **leverage**),
and those with Y values that are not consistent with the regression relation
for the other values (high **residual**) are expected to be influential. The
test the influence of such values, the **Cook statistics** is used.

**Overdispersion**: Dispersion
is a measure of the extent to which data are spread about an average.
Overdispersion is the situation that occurs most frequently in Poisson and
binomial regression when variance is much higher than the mean (normally, it
should be similar). It is evident with a high (>2) residual mean deviance
(which should normally be around one) and the presence of too many outliers. The
reasons for overdispersion may be outliers, misspecification of the model,
variation between the response probabilities and correlation between the binary
responses. It distorts standard error and confidence interval estimations. In
the analysis, overdispersion may be taken into account by estimating a
dispersion parameter.

**Overfitting**: In a **multivariable
model**, having more variables than can be justified from sample size. The
statistical rule of thumb is to have at least ten subjects for each variable
investigated. When fitting models, adding more and more parameters increases
the likelihood of fitting, but this is called overfitting. The resulting model
will be fitting very well (with minimal residuals) but it will be very hard to
replicate in following experiments.

**Overmatching**: When cases
and controls are matched by an unnecessary non-confounding variable, this is
called overmatching and causes underestimation of an association. For example,
matching for a variable strongly correlated with the exposure but not with the
outcome will cause loss of efficiency. Another kind of overmatching is matching
for a variable which is in the causal chain or closely linked to a factor in
the causal chain. This will also obscure evidence for a true association.
Finally, if a too strict matching scheme is used, it will be very hard to find
controls for the study. See **BMJ Statistics Notes**: **Matching**.

**Parameter**: A
numerical characteristic of a "population" specifying a distribution
model (such as normal or Poisson distribution). This may be the mean, variance,
degrees of freedom, the probability of a success in a binomial distribution,
etc. A **p**opulation **p**arameter is usually unknown and estimated as a
**s**tatistic in a random **s**ample from the population. Customarily, it
is denoted by a Greek letter (as opposed to Roman letters/numerals for
statistic(s) in a sample).

**Parsimonious**: The
simplest plausible model with the fewest possible number of variables.

**Pearson's
correlation coefficient (r)**: A measure of the strength of the 'linear'
relationship between two quantitative variables. A major assumption is the
normal distribution of variables. If this assumption is invalid (for example,
due to outliers), the non-parametric equivalent **Spearman's rank correlation**
should be used. The** r** represents C^{2}
obtained from the 2x2 table, corrected for the total sample size. It can then
be calculated as ±(C^{2}/N)^{1/2}.
This formula is equivalent to covariance divided by the product of the standard
deviations of the two variables. The **correlation coefficient**, r, can
take any value between -1 and +1; 0 meaning no "linear" relationship
(there may still be a strong non-linear relationship). It is the absolute value
of r showing the strength of relationship. An associated *P* value can be
computed for the statistical significance (a small *P*
value does not necessarily mean a strong relationship). The square of the r is
r^{2} (r-squared or **coefficient of determination**) which
corresponds to the variance explained by the correlated variable (see **GraphPad Guide to Correlation Parameters and Interpretation of r**). R^{2} is also
used in regression analysis (see **multiple regression correlation coefficient**;
**Online
Correlation & Regression Calculators** at **Vassar College**;
**Excel
Macro for Linear Correlation & Regression)**.

**Pharmacoepidemiology**: Application of
epidemiological reasoning, methods and knowledge to the study of the uses and
effects (beneficial and adverse) of drugs in human populations. A relatively
new field in epidemiology becoming more closely related to pharmacogenetics.
See a review on statistical analysis of pharmacoepidemiological case-control
studies (**Ashby, 1998**).

**Phi coefficient**: A measure of association
of two variables calculated from a contingency table as (X^{2} / N) ^{1/2}.
Its value varies between 0 (no association) and 1 (strongest association) for
2x2 tables where it is an accurate statistics (for larger tables, **Cramer’s V**
is more accurate). In a way, it is a corrected Chi-squared value for the number
of observations. See **Calculator 3** in **Clinical
Research Calculators** at **Vassar**.

**Poisson distribution**: The probability
distribution of the number of (rare) occurrences of some random event in an
interval of time or space. Poisson distribution is used to represent
distribution of counts like number of defects in a piece of material, customer
arrivals, insurance claims, incoming telephone calls, or alpha particles
emitted. A transformation that often changes Poisson data approximately normal
is the square root. See** ****Poisson Distribution****
(QuickTime)**; **GraphPad Poisson Probability Calculator**.

**Poisson
regression**:
Analysis of the relationship between an observed count with a Poisson
distribution (i.e., outcome variable) and a set of explanatory variables. In
general it is appropriate to fit a Poisson model to the data if the sample size
is > 100 and the mean for the occurrence of the event is <0.10xN.

**Polynomial**: A sum of
multiples of integer powers of a variable. The highest power in the expression
(n) is the degree of the polynomial. When n=1, for example, f(x)=2x^{1}+3,
this is a linear expression. If n=2, it is quadratic (for example, x^{2}
+ 2x + 4); if n=3, it is cubic, if n=4, it is quartic and if n=5, it is
quintic.

**Polytomous
variable**:
A variable with more than two levels. If there are two levels it is called
dichotomous (as in the most common form of **logistic regression**).

**Population**: The
population is the universe of all the objects from which a sample could be
drawn for an experiment.

**Population attributable risk**: The
proportion of a disease in a specified population attributable to a specific
factor (such as a genetic risk factor).

**Population stratification (substructure)**:
An^{
}example of 'confounding by ethnicity' in which the co-existence of
different disease rates and allele frequencies within population sub-sections
lead to an association between the two at a whole population level. Differing
allele frequencies in ethnically different strata in a single population may
lead to a spurious association or mask an association by artificially modifying
allele frequencies in cases and controls when there is no real association (for
this to happen, the subpopulations should differ not only in allele frequencies
but also in baseline risk to the disease being studied). Case-control
association studies can still be conducted by using
genomic controls (**Devlin, 1999**; **Pritchard, 1999**) even when population
stratification is present. The software **STRUCTURE
& STRAT** or **ADMIXMAP** can be used to analyze
case-control data with genomic control. See **Cardon
& Palmer, 2003 **for an example of spurious association
due to population stratification. See also **Genetic Epidemiology**.

**Positive predictive value**: Probability of a true
positive as in a person identified as diseased by a test is really diseased
(see also **negative predictive value**).

**Post
hoc test**:
A test following another one. The most common example is performing multiple
comparisons between groups when the overall comparison between groups shows a
significant difference. For example, when an **ANOVA** analysis yields a
small *P* value, *post hoc* tests (such as Newman-Keuls, Duncan's or **Dunnett's**
tests) are done to narrow down exactly which pairs differ most significantly
(similarly, **Dunn's test** is done in a non-parametric **ANOVA**
setting) (**GraphPad
Post ANOVA Test Calculator**). In genetic
association studies, multiple comparisons are justified only when performed as
a *post hoc* test following a significant deviation in overall gene/marker
frequencies (see **HLA and Disease Association Studies**).

**Power of a statistical test**: See **Statistical
Power**.

**Precision**: The degree to which a parameter
(like the **mean**) is immune from random variation. Precision is quantified
by the **confidence interval; **precision increases by increasing the sample
size. Precision is different from **accuracy **(which has to do systematic
error** **or** bias**).

**Predictor (explanatory, independent) variable**:
The variable already in hand in the beginning of an experiment or
observation and whose effect on an outcome variable is being modeled.

**Predictive
value**:
The probability that a person with a positive test if affected by the disease
(positive predictive value) or the probability that a person with a negative
test does not have the disease (negative predictive value). Estimation requires
sensitivity, specificity and disease prevalence.

**Prevented
fraction**:
The amount of a health problem that actually has been prevented by a prevention
strategy in real world.

**Probability**: The ratio
of the number of likely outcomes to the number of possible outcomes.

**Probability
density function**: When a curve is used to model the variation in a population
and the total area between the curve and the x-axis is 1, then the function
that defines the curve is a probability density function.

**Probability
distribution function**: A function which gives for each number x, the
probability that the value of a continuous random variable X is less than or
equal to x. For discrete random variables, the probability distribution
function is given as the probability associated with each possible discrete
value of the variable.

**Probability
vector**:
Any vector with non-negative entries whose sum is equal to 1.0. See **Wikipedia**.

**Proportional
odds ratio**:
When the response variable in an ordered/ordinal logistic regression model has
more than two ordered response categories, odds ratio obtained for each
category is called a proportional odds ratio. See **UCLA Stata**:
**Ordinal Logistic Regression**; **Lecture note on Logistic Regression**.

*P*** value (SP =
significance probability)**: The *P* value gives an idea on the
strength of evidence against the null hypothesis, i.e., departure from the null
hypothesis when the null hypothesis is true (the larger the *P* value, the
larger the probability of type I error for random sampling error). When the *P*
value is larger than 0.05, the result is considered more likely to be due to
random error (false-positivity; a) rather than a true effect/difference. The *P*
value does not measure the magnitude of effect (that is measured by the **effect
size**). The *P* value represents the probability of making an error in
accepting the alternative hypothesis (or rejecting the null hypothesis) for the
presence of an association/effect. For example, the *P *level of 0.01
(i.e., 1/100) indicates that assuming there was no relation between those
variables whatsoever (the null hypothesis is correct), and we were repeating
experiments like ours one after another, we could expect that approximately in
every 100 replications of the experiment, there would be one in which the
relation between the variables in question would be
equal to or more extreme than what has been found (in other words, random
sampling from identical populations (i.e., null hypothesis is true) would lead
to a difference larger than you observed only in 1% of experiments). Thus, it
is the probability of observing a statistic (in the sample) that extreme if the
null hypothesis is true (__not__ the probability that the null hypothesis is
true). In the interpretation of a *P* value, it is important to know the
accompanying measure of association and the biological/clinical significance of
the significant difference. However small, a *P* value does not indicate
the size of an effect (odds ratio/relative risk/hazard ratio/correlation
coefficient do). A *P* value >0.05 does not necessarily mean lack of
association or no effect. It does so only if there is enough statistical power
(i.e., >80%) to detect an effect/association. Most statistical
non-significance is due to lack of power to detect an association (poor
experimental design). Statistical significance threshold (the pre-determined *P*
value for statistical significance or a)
is one of the determinants of **statistical power**. Both 'p' and 'P' are
used to indicate significance probability, but the international standard is *P*
(capital italic). See **Interpreting Statistical P Values**;
review of hypothesis testing (

**Qualitative**:
Qualitative (**categorical**) variables define different categories or
classes of an attribute. Examples are gender, blood groups or disease states. A
qualitative (categorical) variable may be **nominal** or **ordinal**. When
there are only two categories, it is termed binary (like sex, dead or alive).

**Quantitative**:
Quantitative variables are variables for which a numeric value representing an
amount is measured. They may be discrete (for example, taking values of
integers) or continuous (such as weight, height, blood pressure). If a
quantitative variable is categorized, it becomes an **ordinal variable**.

**R**: A language and environment for statistical computing and
graphics.
R is an open platform and offers thousands of programs (called libraries) to
achieve a wide variety of statistical, graphical, biological tasks. It has a
large number of (statistical) genetics and statistics
**libraries**. To learn how to use R (__not__ how to program
R!), see this self-paced **R course** and the
links within.

**R ^{2}
(R-squared)**: See

**Random sampling**: A method of selecting
a sample from a target population or study base using simple or systematic
random methods. In random sampling, each subject in the target population has
equal chance of being selected to the sample. Sampling is a crucially important
point in selection of controls for a case-control study. The idea with sampling
is to estimate the unknown population parameters from the sample. The sample
should, therefore, be a randomly selected one to represent the whole
population. Even randomly selected samples will be different from each other
than the population itself. This represents the sampling error which can cause
false-positive results (type I error rate or a, which is measured by
the *P* value). By randomization, a balancing out effect is expected:
known and unknown factors that might influence the outcome are assigned equally
to the comparison groups (and minimizes bias and confounding). One disadvantage
of randomization is generation of a potentially large error term. This can be
avoided by using a **block design**. See **Wikipedia**:
**Random Sampling**.

**Randomized
(complete) block design**: An experimental design in which the
treatments in each block are assigned to the experimental units in random
order. Blocks are all of the same size and each treatment appears in the same
number of times within each block (usually once). A different level of the
factor is assigned to each member of the block randomly. The data can be
analyzed using the paired t-test (when there are two units per block) or by
randomized block ANOVA (in blocks of any size). The results are substantially
more precise than a completely randomized design of comparable size. In studies
with a block design, more assumptions are required for the model: no
interactions between treatments and blocks, and constant variance from block to
block.

**RCT**: See **randomized
controlled trial**.

**Randomized
controlled (or clinical) trial** (**RCT**): The favored clinical research
design in which the comparative groups are formed by random allocation of
subjects in an attempt to minimize (selection) bias and to distribute
measurable and unmeasurable confounding factors equally, and double-blinding is
used to conceal allocation of the intervention to reduce a number of biases, leaving
the exposure (usually a new drug or a placebo) whose effect will be measured as
the causal factor on the outcome. RCTs are the highest-ranking clinical
research design in the epidemiologic hierarchy of strength of evidence for
causality. This is because it suffers from minimum bias/confounding, and
isolates the (therapeutic) effect of interventions due to being considered a
controlled design.

**Ratio
variable**:
A quantitative variable that has a zero point as its origin (like 0 cm = 0
inch) so that ratios between values are meaningfully defined. Unlike the **interval
variables**, which do not have a true zero point, the ratio of any two values
in the scales is independent of the unit of measurement. For example, 2/12 cm
has the same ratio as the corresponding values in inch (but the same cannot be
said for 2/12 Celsius and 2/12 Fahrenheit which are interval variables).

**Receiver operating characteristics (ROC) curve
analysis**: Also called discrimination statistics used in diagnostic test
accuracy assessment and the utility of predictive tests (but see **Pencina,
2008**; **Pencina, 2012**). See **ROC**
in **Clinical
Research Calculators** and **Difference
Between the Areas Under Two ROC Curves** at **Vassar**,
**ROC101** by Tom Fawcett. See reviews on ROC
analysis: **Hanley & McNeil. 1982**; **Bewick,
2004**; **Obuchowski, 2005**; **Cook NR, 2007**; **Steyerberg, 2012**. See also the **Supplementary Data File** for **Mamtani,
2006** for the use of **Stata in
ROC analysis**.

**Regression
diagnostics**: Tests to identify the main problem areas in regression
analysis: normality, common variance and independence of the error terms
(epsilon or e); outliers,
influential data points, collinearity, independent variables being subject to
error, and inadequate specification of the functional form of the model. The
purpose of the diagnostic techniques is to identify weaknesses in the
regression model or the data. Remedial measures, correction of errors in the
data, elimination of true outliers, collection of better data, or improvement
of the model, will allow greater confidence in the final product. See also **error
terms**, **residuals** (including **likelihood distance test**), **leverages**
and **Cook statistics**.

**Regression
modeling**:
Formulating a mathematical model of the relationship between a response
(outcome, dependent) variable, Y, and a set of explanatory (predictor,
independent, regressor) variables, x. Depending on the characteristics of the
variables, the choice of model can be simple linear regression, multiple
regression, logistic (binary) regression, Poisson regression, etc. In any
regression problem, the key quantity is the mean value of the outcome variable,
given the value of the independent variable(s). This quantity is called the
conditional mean and expressed as "E (Y½x)" where Y is the
response (outcome), x is the explanatory (predictor) variable. The question is
whether the variable(s) in question tells us more about the outcome variable
than a model that does not include that variable. In other words, whether the
coefficient of the variable(s) is zero and the outcome is equal to a constant
(which is the mean for Y) or not. The aim of model building is to arrive at a
meaningful (say, biologically relevant) and parsimonious model that explains
the data. The model may be linear if the parameters are linear, or
nonparametric if the parameters are not linear. No matter how strong is the
statistical relationship between x and Y, no
cause-and-effect pattern is necessarily implied by the regression models. See **Regression Applet**.

**Regression towards the mean**: See the
explanation and a **simulation **at** Rice
Virtual Lab in Statistics**; and a **Lecture Note**.

**Relative
risk (RR)**:
Also known as **risk ratio**. The RR shows how many times more or less the
individuals with the risk factor are likely to get the disease relative to
those who do not have the risk factor. RR gives the strength of association in
prospective cohort studies. It cannot be estimated in retrospective
case-control studies, and its use to describe the cross-product ratio (as
frequently done in HLA association studies) is inappropriate. See **Calculator 3** in **Clinical
Research Calculators** at **Vassar**. See
also **odds ratio**.

**Repeated
measures design**: In this design, the same experimental unit is subjected
to the different treatments under consideration at different points in time.
Each unit, therefore, serves as a block. If for example, two different
treatments and placebo treatment are applied to the same patient sequentially,
this is a repeated measures design. See also **cross-over design**.

**Resampling
statistics**:
Data-based simulation procedures that sample (with replacement) repeatedly from
observed data to generate empirical estimates of results that would be expected
by chance. Examples include **bootstrapping** and permutation tests. See
also **Online Resampling Book**.

**Residuals**: Residuals
reflect the overall badness-of-fit of the model. They are the differences
between the observed values of the outcome variable and the corresponding
fitted values predicted by the regression line (the vertical distance between
the observed values and the fitted line). In a regression analysis, a large
residual for a data point indicates that the data point concerned is not close
to its fitted value. If there are too many large (standardized) residuals
either the model fitted is not adequate or there is **overdispersion** of
the data. Ideally, the residuals should have constant variance along the line.
This can be checked by a normal probability plot of the residuals. In the plot
of residuals against the explanatory variable (or the fitted values), there
should not be any pattern if the assumption of constant variation is met, i.e.,
residuals do not tend to get larger as the variable values get larger or
smaller (see also **likelihood distance test**).

**Residual
plot**:
A graph that plots residuals against fitted values. It is used to check equal
variance assumption of the error terms in linear regression. Residual analysis
for logistic regression is more difficult than for general linear regression
models because the responses Y* _{i}* can take only the values 0
and 1. Consequently, the residuals will not be normally distributed. Plots of
residuals against fitted values or explanatory variables will be uninformative.
Residual plots are generally unhelpful for

**Residual
(error) sum of squares (RSS)**: The measure of within treatment groups sum of
squares (variability) in ANOVA. It is the deviation around the fitted
regression line. The sum of squared differences between each observed Y value
(Y* _{i}*) and the fitted Y value (Y

**Regression
(explained) sum of squares (ESS)**: The measure of between treatment
groups sum of squares (variability) in ANOVA. It is the deviation of fitted
regression value around mean. The sum of squared differences between each
fitted Y value (Y* _{i}* -hat) and the overall mean of the Y values
equals to the explained (regression) sum of squares. The sum of ESS and RSS
gives the total sum of squares

**Risk
ratio (relative risk)**: The risk ratio is the percentage difference
in classification between two groups obtained as the ratio of two risks or
proportions. For example, the proportion of people recovering after stroke with
one treatment equals 0.10, while the proportion after a different treatment
equals 0.16. The risk ratio equals 0.625 (0.10/0.16); 37.5% ((1-0.625)*100 or
(0.16-0.10)/0.16) fewer patients treated by the first method recover. The risk
ratio takes on values between zero ('0') and infinity. One ('1') is the neutral
value and means that there is no difference between the groups compared. See
also **relative risk**.

**Robustness**: A
statistical test or procedure is robust when violation of assumptions has little
effect on the results. Student's t-test, for example, is robust against
departures from normality.

**R
project for statistical computing**: R is a
language and environment for statistical computing and graphics which can be
seen as a different implementation of the S language. R and a comprehensive set
of programs written for a variety of statistical analysis are all available
freely. See the **R
Project Website** & **List
of Contributed R Packages**. See also **OpenIntro
Statistics Book (based on R)**; **OpenIntro Advanced High School Statistics Book**;
**OpenIntro Statistics with Randomization and Simulation**.

**Sample
size determination**: Mathematical process of deciding how many
subjects should be studied (at the planning phase of a study) to observe the
hypothesized effect with statistical significance. If the sample size is
insufficient, the effect may be observed, but the statistical significance will
not be reached (and will be called __not__ detected [with statistical
significance]). Among the factors to consider are the incidence or prevalence
of the condition, the magnitude of difference expected between comparison
groups (i.e., effect size), the power that is desired (usually 80% due to allowing
20% type II error (false-negativity or b) rate) and the allowable magnitude of type I error (pre-determined significance probability;
alpha value; usually 0.05). **Sample size calculator****, ****sample size calculation**.

**SAS **(Statistical Analysis System): A
comprehensive computer software system for data processing and analysis. It can
be used for almost any type of statistical analysis. Produced by **SAS Institute**.
See **SAS
Learning Resources (UCLA)**; **SAS Tutorials**; **Getting Started with SAS Enterprise Guide (Free Online
Course)**; **SAS e-Learning**; **SAS Genetic Software** and **Genetic Data Analysis**.

**Saturated model**: A model
that contains as many parameters as there are data points. This model contains
all main effects and all possible interactions between factors. For categorical
data, this model contains the same number of parameters as cells and results in
a perfect fit for a data set. The (residual) deviance is a measure of the
extent to which a particular model differs from the saturated model.

**Scales of measurement**: The type of data is always
one of the following four scales of measurement: nominal, ordinal, interval, or
ratio. Each of these can be discrete or continuous.

**Schoenfeld residual test**: One of
the diagnostic tests to check the proportionality assumption (covariates are
time independent) in proportional hazard modeling. A variation is the use of
scaled Schoenfeld residuals (see **Tests of Proportionality in SAS, Stata, R and SPLUS**).

**Sensitivity**: Sensitivity is the proportion of
true positives that are correctly identified by a diagnostic test. Those that
produce few false negatives have higher sensitivity. See **Sensitivity and Specificity by Altman & Bland, BMJ 1994;**
**Interpreting
Diagnostic Tests** and **DAG-STAT**;
calculators: **Vassar**:
**Clinical
Calculator 1** & **Clinical Calculator 2**; **SISA:
Diagnostic Effectiveness Tests**. See also **specificity.**

**Sign test**: A test based on the probabilities
of different outcomes for any number of pluses and minuses, i.e., observations
below or above a prespecified value. The sign test can be used to investigate
the significance of the difference between a population median and a specified
value for it, or between the observed sex/transmission ratio and the 50:50
expected value. It can also be used for paired data. This time, the differences
between the pairs will be either negative or positive, and the smaller of the
two total negatives or positives plus the total number of pairs will form the
test statistics. For example, when the total number is 20, if the number for
the less frequent sign is 5 or smaller, *P* < 0.05 (two-tailed). A sign
test in disguise is **McNemar's test**, which is used for paired data for
dichotomous response.

**Simple
linear regression model**: The linear regression model for a normally
distributed outcome (response) variable and a single predictor (explanatory)
variable. The straight line models the mean value of the response variable for
each value of the explanatory variable. The major assumption is constant
variation of residuals along the fitted line which points out that the model is
equally good across all x values. The null hypothesis stating that the
explanatory variable has no effect on the response (in other words, the slope
of the fitted line is zero) can be tested statistically. The two main aims of
regression analysis are to predict the response and to understand the
relationships between variables. As in all linear models, the error term (shown
as W_{i} or e_{i}) is
additive (as opposed to multiplicative, i.e., y_{i} = a + bx_{i}
+ e_{i}) and
independent, and they are assumed to have a normal distribution. As an
exception, the simple linear regression is a special case for **generalized
linear models**.

**Skewness**: The degree
of (lack of) asymmetry about a central value of a distribution. A distribution
with many small values and few large values is positively (right) skewed (long
tail in the distribution curve or stem plot is to the right); the opposite
(left tail) is negatively (left) skewed. The measures of location median,
mid-interquartile range (midQ) and mid-range decrease in this order for a
left-skewed distribution. (**Definition of Kurtosis and Skewness**; **Online Skewness-Kurtosis Calculator**; see also **kurtosis**).

**Sparseness**: A
contingency table is sparse when many cells have small values. When N is the
total sample size, and r and c are the number of rows and columns, N / rc is an
index of sparseness. Smaller values refer to more sparse tables. Sparse tables
often contain zero values (empty cell).

**Spearman's
rank correlation**: A non-parametric **correlation
coefficient** **(rho)** that is
calculated by computing the **Pearson's correlation coefficient** **(r)**
for the association between the ranks given to the values of the variables
involved. It is used for ordinal data and interval/ratio data. It is * not*
appropriate to take the square of Spearmen’s correlation coefficient rho to
obtain

**Specificity**: Specificity is the proportion of
true negatives that are correctly identified by the test. Those that produce
few false positives have higher specificity. **Sensitivity and Specificity by Altman & Bland, BMJ 1994**;
**Interpreting
Diagnostic Tests** and **DAG-STAT**.
Calculators: **Vassar**:
**Clinical
Calculator 1** & **Clinical Calculator 2**; **SISA:
Diagnostic Effectiveness Tests**. See also **sensitivity**.

**Square
root transformation**: Usually used for highly positively skewed data,
but especially in transforming Poisson counts to normality.

**Standard
deviation**:
Like **variance**, the standard deviation (SD) is a measure of spread
(scatter) of a set of data. Unlike variance, which is expressed in squared
units of measurement, the SD is expressed in the same units as the measurements
of the original data. It is calculated from the deviations between each data
value and the sample **mean**. It is the square root of the variance. For
different purposes, n (the total number of values) or n-1 may be used in
computing the variance/SD. If you have a SD calculated by dividing by n and
want to convert it to a SD corresponding to a denominator of n-1, multiply the
result by the square root of n/(n-1). If a distribution's SD is greater than
its **mean**, the mean is inadequate as a representative measure of central
tendency. For normally distributed data values, approximately 68% of the
distribution falls within ± 1 SD of the mean, 95% of the distribution
falls within ± 2 SDs of
the mean, and 99.7% of the distribution falls within ± 3 SDs of the mean
(empirical rule). SD should not be confused with the **standard error of the mean (SEM)**,
which quantifies how precise the mean is (See **Normal Distribution**; **Online Calculator for Standard Deviation; Standard
Deviation calculation by GraphPad QuickCalc**). See also: **Altman & Bland: Statistical Notes - Standard deviations
and standard errors. BMJ 2005.331:903**.

**Standard
error**:
The standard error (SE) or as commonly called the standard error of the mean
(SEM) is a measure of the extent to which the sample mean deviates from the true
but unknown population mean. It is the **standard deviation** (SD) of the
random sampling distribution of means (i.e., means of multiple samples from the
same population). As such, it measures the precision of the statistic as an
estimate of a population. The (estimated) SE/SEM is dependent on the sample
size. It is inversely related to the square root of the sample size:

(estimated)
SE = SD / (N)^{1/2}

The
true value of the SE can only be calculated if the SD of the population is
known. When the sample SD is used (as almost always), it is an estimate and
should be called estimated standard error (ESE). When the sample size is
relatively large (N ³
100), the sample SD provides a reliable estimate of the SE. See the explanation
of **the difference between SD and SEM** (**Standard Error calculation by GraphPad QuickCalc**).
See also: **Altman
& Bland: Statistical Notes - Standard deviations and standard errors. BMJ
2005.331:903**.

**Standard
residual**:
The standardized **residual** value (observed minus predicted divided by the
square root of the residual **mean square**).

**Stata**: A powerful
statistical package particularly useful for epidemiologic and longitudinal data
management and analysis. It is mainly a command driven program produced by **Stata
Corporation**. See the list of **Stata Capabilities**, **Stata Starter Kit** with **Learning Modules** by **UCLA**; **What Stat to Use (Stata)**; **Tutorial by University of Essex**; **Tutorial by Princeton University**; **Stata Highlights by Notre Dame University**;** Tutorial by Carolina Population Center**; **Stata Refresher by Syracuse University**; **Stata
for Researchers by Wisconsin University**.

**Statistic**: A
numerical measure that describes a variable (characteristic) of a
representative sample (randomly drawn from a population) to estimate the
unknown population parameter.

**Statistical Power**: The probability that the
hypothesized and observed effect will be statistically significant at a given
significance level is called the power of the test. This is equal to the
probability of rejecting the null hypothesis (i.e., getting a *P* value
less than 0.05) when it is untrue (the hypothesized effect is present), i.e.,
making the correct decision. It is 1 minus the probability of a type II error (b or false-negativity
rate). The true differences between the samples compared (**effect size**), the
sample size and the significance threshold chosen are determinants of the power
of a statistical test. Ideally, power should be at least 0.80 to detect a
reasonable departure from the null hypothesis (allowing 20% false-negativity
rate or b). Insufficient
statistical power is one of the major statistical errors contributing to a bad
research design. See power calculators: **PS: Power and Sample Size Calculation**; **G*Power 3 (User Guide)**;
**General
Statistical Calculators Including a Power Calculator (UCLA)**;
**Russ Lenth's Power Calculator (JAVA-based)**;
**Statistical Power Calculator for Frequencies**;** Retrospective Power Calculation**; **General
Power Calculator**; **Power Calculation for Logistic Regression (including
Interaction)**; **Power Calculation for t-test (SISA)**; **Genetic
Power Calculator**; **Power for
Genetic Association Analyses (PGA) @ NCI**; **Quanto** (sample size and power calculation
for association studies of genes, gene-environment or gene-gene interactions).

**Stepwise
regression model**: A method in multiple regression studies aimed to find the
best model. This method seeks a model that balances a relatively small number
of variables with a good fit to the data by seeking a model with high R^{2}_{a}
(the most parsimonious model with the highest percentage accounted for). The stepwise
regression can be started from a null or a full model and can go forward or
backward, respectively. At any step in the procedure, the statistically most
important variable will be the one that produces the greatest change in the
log-likelihood relative to a model lacking the variable. This would be the
variable, which would result in the largest likelihood ratio statistics, *G*
(a high percentage accounted for gives an indication that the model fits well).
See also **multiple regression correlation coefficient - R ^{2}**.

**Stochastic
model**:
A probability model that includes chance events in the form of random
measurement error or uncertainty. In a deterministic model, however, random
error is inconsequential or nonexistent. See **Wikipedia**:
**Stochastic Modeling**.

**Stratum** (plural strata):
When data are stratified according to its characteristics, each subgroup is a
stratum.

**Student's
t-test**:
A parametric test for the significance of the difference between means (**two-samples t-test**) or between a mean and a hypothesized value (**one-sample
t-test**). One assumption is that the
observations must be normally distributed, and the ratio of variances in two
samples should not be more than three. If the assumptions are not met, there
are non-parametric equivalents of the t-test to use (see for example, **Wilcoxon's
Test**). It is inappropriate to use the t-test for multiple comparisons as a ** post
hoc test**. The t-test for independent samples tests whether or not two
means are significantly different from each other but only if they were the
only two samples taken (

**Subgroup analysis**: Analysis of subgroups of a
sample either because of a prior hypothesis (gender or age-specific
effect/association) or as a fishing expedition / data dredging. This practice
increases type I error (false-positivity) rates (also called *P* hacking).
See a commentary by **Sleight, 2000**.

**Survival Analysis**: See **Superlectures** on ‘Survival Analysis’; **A Primer on Survival Analysis (J Nephrol 2004)**;
**Tutorials on Survival Analysis in Br J Cancer 2003: Part I - II - III - IV**; ‘**Understanding Survival Curves**’ at NMDP
website; **Survival Curves** and **Comparing
Survival Curves** in **Intuitive
Biostatistics**; **BMJ Statistics Notes**: **Time to Event (Survival) Data -** **Survival Probabilities (the Kaplan-Meier method)
- Logrank Test**; Survival Analysis by **STATA** and **SAS**; **Power Calculator for Survival Outcomes**, **PS: Power and Sample Size Calculation**. **Online
survival analysis** at **VassarStats**. For a comprehensive review, see
**Lee & Go, 1997**.

**Survival function**:
A time to failure function that gives the probability that an individual
survives past a time point (does not experience an event like death,
metastasis, conception etc)). Where the event is death, the value of the
survival function at time T is the probability that a subject will die at some
time greater than T. The survival function always has a value between 0 and 1
and is nonincreasing.

**Synergism**: A joint
effect of two treatments being greater than the sum of their effects when
administered separately (positive synergism) or the opposite (negative
synergism).

**Theta
(****q****)**: Used to
denote recombination fraction (in statistical genetics).

**Transformations
(ladder of powers)**: Transformation deals with non-normality of
the data points and non-homogeneous variance. The power transformations form
the following ladder: ..., *x*^{-2}, *x*^{-1}, *x*^{-1/2},
log *x*, *x*^{1/2} **;** *x*^{1}, *x*^{2},
*x*^{3}, ..... Provided *x* > 1, powers below 1 (such as *x*^{1/2}
or log *x*) reduce the high values relative to the low values as in
positively skewed data, whereas, powers above 1 (such as *x*^{2})
have the opposite effect of stretching out high values relative to low ones, as
in negatively skewed data. All power transformations are monotonic when applied
to positive data (they are either increasing or decreasing, but not first
increasing and then decreasing, or vice versa). The **square root
transformation** often renders Poisson data approximately normal.

**Transmission
Disequilibrium Test** (TDT): A family-based study to compare the
proportion of alleles transmitted (or inherited) from a heterozygous parent to
a disease-affected child. Any significant deviation from 0.50 in transmission
ratio implies an association (**Spielman, 1993** & **1994**).

**Treatment**: In
experiments, a treatment is what is administered to experimental units
(explanatory variables). It does not have to be a medical treatment.
Fertilizers in agricultural experiments; different books and multimedia methods
in teaching; and chemotherapy of bone marrow transplantation in the treatment
of leukemia are examples of treatments in regression analysis.

**Trend
test for counts and proportions**: A special application of the
Chi-squared test (with a different formula) for ordinal data tabulated as a 2xk
table. It should be used when the intention is not just to compare the
differences between the two groups but to see whether there is a consistent
trend towards decrease or increase in the difference between the groups. An
example is the association of parental HLA sharing (sharing one-to-four
antigens in two loci) with fetal loss in a case-control study (those with
recurrent miscarriages and normal fertile couples). In genetic studies, the
additive model analysis is done by trend test for counts in cases and controls
(2 columns) of three genotypes (3 rows): wildtype homozygous (0 variant
allele), heterozygote (1 variant allele) and variant homozygote (2 variant
alleles). A frequent application is the analysis of dose-response relationships
in toxicology and pharmacology. The Chi-squared test for trend has one degree
of freedom. The associated *P* value obtained by the Chi-squared for trend
test (1 df) is always smaller than the corresponding *P* value of an
ordinary Chi-squared test (2 df) for departure if indeed there is a trend. The trend test for counts and proportions is called
Cochrane-Armitage trend test. Alternative tests for the analysis of trend are **Wilcoxon-Mann-Whitney
test **or the t-test with use of ordered scores, and the **Jonckheere-Terpstra test** as
a non-parametric test for ordered data (see **Trend for Binomial Outcome** in the manual of
Epi Info;** ****Epi Info Freeware for Trend Test** (Trend Test
in StatCalc or **Open-Epi Online**); **Trend Test in Excel** and **with XLStat**;
**InStat** fully functional demo version for Trend
Test; **Trend Tests in Stata**).

**t-statistics**: Defined as
difference of sample means divided by standard error of difference of sample
means (see **Student's t-test**).

**Two-way
ANOVA**:
This method studies the effects of two factors (with several levels) separately
(main effect) and, if desired, their effect in combination (interaction).

**Type
I error**:
If the null hypothesis is true but we reject it (wrongly) this is an error of
first kind or type I error (also called a error). This results in a false positive
finding. The probability of (type I) error is assessed by the *P* value
(i.e., the probability of false-positivity) . Acceptable type I error rate is
usually up to 5%.

**Type
II error**:
If the null hypothesis is accepted when it is in fact wrong (missing an
existing effect), this is an error of the second kind or type II error (also
called b error).
This results in a false negative result. Type II error rate is (1 - statistical
power), therefore, usually 20% (because statistical power is usually set at
80%). By setting statistical power at 80%, we acknowledge that up to 20% of the
time, we will miss an existing effect (i.e., it will be observed, but will not
reach statistical significance [i.e., will __not__ be detected]).

**Unreplicated
factorial**:
A single replicate of a 2^{k} design (where each of k factors of
interest has only two levels).

**Variable**: A
characteristic that varies among experimental units (subjects) or from time to
time. A variable may be **quantitative** or **categorical**. A
quantitative variable is either **discrete** (assigning meaningful numerical
values to observations: number of children, dosage in mg) or **continuous**
(such as height, weight, temperature, blood pressure; also called **interval
variable**). A categorical variable is either **nominal** (assigning
observations to categories: gender, treatment, disease subtype, groups) or **ordinal**
(ranked variables: low, median, high dosage). Conventionally, a random variable
is shown by a capital letter, and the data values it takes by lower case
letters.

**Variance**: The major
measure of variability for a data set. To calculate the variance, all data
values, their mean, and the number of data values are required. It is expressed
in the squared unit of measurement. Its square root is the **standard
deviation**. It is symbolized by s^{2} (a parameter) for a
population and *S*^{2} (a statistic) for a sample (**Online Calculator for Variance and Other Descriptive
Statistics**).

**Variance
ratio**:
Mean square ratio obtained by dividing the mean square (regression) by mean
square (residual). The variance ratio is assessed by the F-test using the two
degrees of freedom (k-1, N-k).

**Wald
test**:
A test for the statistical significance of a regression coefficient. It is
obtained by comparing the maximum likelihood estimate of the slope parameter
(expected b_{1}) to an
estimate of its standard error. The resulting ratio (*W*), under the
hypothesis that b_{1} = 0, will
follow a standard normal distribution. The two-tailed *P* value will be
found from the Z table corresponding to *P* ( ç Z ç > *W*).
It is not more reliable than the **likelihood ratio test** (**deviance
difference**).

**Welch-Satterthwaite
t-test:**
The Welch-Satterthwaite t-test is an alternative to the pooled-variance t-test,
and is used when the assumption that the two populations have equal variances
seems unreasonable. It provides a t statistic that asymptotically approaches a
t-distribution, allowing for an approximate t-test to be calculated when the
population variances are not equal (**Online Welch's Unpaired
t-test**; **t-Test Assuming
Unequal Sample Variances** at **Vassar**).

**Wilcoxon matched pairs signed rank T-test**:
A non-parametric significance test analogous to paired t-test. Most suitable
for

**William's correction **(for** G statistics**)**:**
This is equivalent to Yates' continuity correction for Chi-squared test but
used in likelihood ratio (G) statistics for 2x2 tables (**Online G
Statistics**** **with
William's correction).

**Woolf-Haldane analysis**: A method
first described by Woolf and later modified by Haldane for the analysis of 2x2
table and relative incidence (**relative risk**) calculation. It is the preferred
method for relative risk calculation when one of the cells has a zero using the
formula: RR = (2a+1)(2d+1) / (2b+1)(2c+1). Since it is a modification of the
cross-product ratio, it should be called **odds ratio**. For details and
references, see **Statistical Analysis in HLA and
Disease Association Studies**.

**Yates's
correction**:
The approximation of the Chi-square statistic in small 2x2 tables can be
improved by reducing the absolute value of differences between expected and
observed frequencies by 0.5 before squaring. This correction, which makes the
estimation more conservative, is usually applied when the table contains only
small observed frequencies (<20). The effect of this correction is to bring
the distribution based on discontinuous frequencies nearer to the continuous
Chi-squared distribution. This correction is best suited to the contingency
tables with fixed marginal totals. Its use in other types of contingency tables
(for independence and homogeneity) results in very conservative significance
probabilities. This correction is no longer needed since exact tests are
available.

**Z
score**:
The Z score or value expresses the number of standard errors by which a sample
mean lies above or below the true population mean. Z scores are standardized
for a distribution with mean = 0 and standard
deviation = 1 (**Corresponding P values for Z**;

*MAJOR RESOURCES IN BIOSTATISTICS*

**Armitage P & Colton T.
****Encyclopedia
of Biostatistics**. **Volumes
1-8. John Wiley & Sons, 2005**

**Bland M. ****An Introduction to Medical Statistics****.
3rd Edition. Oxford Medical Publications, 2000**

**Campbell MJ & Machin
D. ****Medical Statistics: A Common Sense Approach****.
Wiley, 2002**

**Daly LE & Bourke GJ. ****Interpretation
and Uses of Medical Statistics****.
5th Edition. Blackwell Scientific Publications, 2000**

**Motulsky H. ****Intuitive Statistics****.
4 ^{th} Edition. OUP, 2017**

**Norman GR & Streiner
DL. ****PDQ Statistics****.
3 ^{rd} Edition. BC Decker, 2003**

**Rosner B. ****Fundamentals
of Biostatistics****. 8th
Edition. Cengage, 2015**

**Sokal RR, Rohlf FJ. ****Biometry****.
4th Edition. WH Freeman & Company, 2011**

**Zar JH. ****Biostatistical
Analysis****. 5th Edition. Pearson,
2010**

**Elston, Olson &
Palmer. ****Biostatistical Genetics and Genetic Epidemiology****.
Wiley, 2002 **

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