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COMMON CONCEPTS IN STATISTICS

M.Tevfik Dorak, BA (Hons), MD, PhD

See also Common Terms in Mathematics; Statistical Analysis in HLA-Disease Association Studies;

Epidemiology (incl. Genetic Epidemiology Glossary)

To reach the notes of the workshop at BSHI 2002 meeting, click here (PowerPoint file)

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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.

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. (From the Glossary of Disease Control Priorities Project.)

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); see also AMOVA Lecture Note (EEB348).

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