Choosing Statistical Tests — A Guide

Correct selection of a statistical test is one of the most important elements of data analysis in scientific work. An improperly chosen test can lead to erroneous conclusions and undermine the credibility of the entire study. In this article, we present a practical guide to the most commonly used statistical tests.

The first step is determining the type of variables. Variables can be quantitative (continuous or discrete) or qualitative (nominal or ordinal). The type of variable determines which statistical methods will be appropriate — parametric tests require quantitative variables with approximately normal distribution, while non-parametric tests are more versatile.

For comparing two independent groups with quantitative data of normal distribution, we use Student's t-test. If the assumptions of a parametric test are not met, the alternative is the Mann-Whitney U test. For comparing more than two groups, we use analysis of variance (ANOVA) or its non-parametric counterpart — the Kruskal-Wallis test.

For qualitative data, the most commonly used tests are the chi-square test and Fisher's exact test. The chi-square test checks the independence of two categorical variables, while Fisher's test is preferred with small sample sizes.

Correlations between quantitative variables are assessed using Pearson's correlation coefficient (for normally distributed data) or Spearman's (for data that doesn't meet the normality assumption). Linear regression allows modeling the relationship between a dependent variable and one or more independent variables.

Remember about corrections for multiple comparisons. When performing multiple tests simultaneously, the risk of Type I error increases. Popular correction methods include Bonferroni correction, Holm-Bonferroni procedure, and false discovery rate (FDR) control using the Benjamini-Hochberg method.