Normality and Sample Size Do Not Matter for the Selection of an Appropriate Statistical Test for Two-Group Comparisons

Abstract

Abstract. Many applied researchers are taught to use the t-test when distributions appear normal and/or sample sizes are large and non-parametric tests otherwise, and fear inflated error rates if the “wrong” test is used. In a simulation study (four tests: t-test, Mann-Whitney test, Robust t-test, Permutation test; seven sample sizes between 2 × 10 and 2 × 500; four distributions: normal, uniform, log-normal, bimodal; under the null and alternate hypotheses), we show that type 1 errors are well controlled in all conditions. The t-test is most powerful under the normal and the uniform distributions, the Mann-Whitney test under the lognormal distribution, and the robust t-test under the bimodal distribution. Importantly, even the t-test was more powerful under asymmetric distributions than under the normal distribution for the same effect size. It appears that normality and sample size do not matter for the selection of a test to compare two groups of same size and variance. The researcher can opt for the test that fits the scientific hypothesis the best, without fear of poor test performance.