Table 2 

Classification of statistical methods (after Emerson and Colditz, 1983) [10] 

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 
Chisquare tests, Fisher's test, McNemar's test 
Multiway tables 
MantelHaenszel procedure, loglinear models 
Epidemiological studies 
Relative risk, odds ratio, log odds, measures of association, sensitivity, specificity 
ttests 
Onesample, matched pair, and two sample t tests 
Pearson correlation 
Classic productmoment correlation 
Simple linear regression 
Leastsquares 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 Ftests 
Multiple comparisons 
Procedures for handling multiple inferences on same data sets (e.g., Bonferroni techniques, Scheffe's contrasts, Duncan's multiple range procedures, NewmannKeuls procedure) 
Nonparametric tests 
Sign test, Wilcoxon signed ranks test, Mann Whitney test, Spearman's rho, Kendall's tau, test for trend 
Life table 
Actuarial life table, KaplanMeier 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 
Costbenefit 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 


Rigby et al. BMC Medical Research Methodology 2004 4:28 doi:10.1186/14712288428 