Robust tests should be the default, not the backup

The assumptions of standard tests such as the t-test, ANOVA and ordinary least squares regression are frequently violated. This can impact the desired error rates in statistical hypothesis testing. Robust tests like the Mann–Whitney U test and robust linear regression do not rely on assumptions such as normality and equal variances. Using them can counteract non-replicated findings that are just due to data anomalies, such as extreme values and outliers, which occur differently across studies. Employing them from the outset bypasses the pitfalls of deciding on the usability of a standard test with data. In this opinion piece, I summarize the epistemic benefits of robust alternatives. Restricting to a robust test instead of conducting it in addition to the standard test avoids generating multiple results, thus counteracting fishing for the desired, which can occur subtly. From a practical standpoint, running a single test simplifies analysis, and many robust methods are readily available in R. However, it is important to understand what a robust method does and what it is actually robust against. I also address common defenses of standard tests, discuss why they remain widespread, and suggest how these arguments should be countered.