# Table 2 Classification of statistical methods (after Emerson and Colditz, 1983) 

Category Brief description
No statistical methods or descriptive statistics No statistical content, or descriptive statistics only (e.g., percentages, means Standard deviations, standard errors, histograms
Contingency tables Chi-square tests, Fisher's test, McNemar's test
Multiway tables Mantel-Haenszel procedure, log-linear models
Epidemiological studies Relative risk, odds ratio, log odds, measures of association, sensitivity, specificity
t-tests One-sample, matched pair, and two sample t- tests
Pearson correlation Classic product-moment correlation
Simple linear regression Least-squares regression with one predictor and one response variable
Multiple regression Includes polynomial regression and stepwise regression
Analysis of variance Analysis of variance, analysis of covariance, and F-tests
Multiple comparisons Procedures for handling multiple inferences on same data sets (e.g., Bonferroni techniques, Scheffe's contrasts, Duncan's multiple range procedures, Newmann-Keuls procedure)
Non-parametric tests Sign test, Wilcoxon signed ranks test, Mann- Whitney test, Spearman's rho, Kendall's tau, test for trend
Life table Actuarial life table, Kaplan-Meier estimates of survival
Regression for survival Includes Cox regression and logistic regression
Other survival analysis Breslow's Kruskal Wallis, log rank, Cox model for comparing survival
Adjustment & standardisation Pertains to incidence rates and prevalence rates
Sensitivity analysis Examines sensitivity of outcome to small changes in assumptions
Power Loosely defined, includes use of the size of detectable (or useful) difference in determining sample size
Transformation Use of data transformation (e.g., logs) often in regression
Cost-benefit analysis The process of combining estimates of cost and health outcomes to compare policy alternatives
Other Anything not fitting the above headings includes cluster analysis, discriminant analysis, and some mathematical modelling