Some people argue that the Welch’s t-test should be the default choice for comparing the means of two independent groups since it performs better than the Student’s t-test when sample sizes and variances are unequal between groups, and it gives identical results when sample sizes are variances are equal. In practice, when you are comparing the means of two groups it’s unlikely that the standard deviations for each group will be identical. This makes it a good idea to just always use Welch’s t-test, so that you don’t have to make any assumptions about equal variances.

Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

Nonparametric statistics are often preferred to parametric tests when the sample size is small and the data are skewed or contain outliers.

In a new paper appearing in Communications Biology

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