Dear Editor-in-Chief,

Researchers, professionals, and students in training in the health and social sciences fields need to make decisions based on sound scientific evidence. Generally, public mental health policies are based on the strength and robustness of scientifc findings. A critical aspect of research at the methodological level has to do with statistical analyses.

Often, to test hypotheses from a frequentist model approach, many of the performed analyses are conditioned on the assumption of normality of the data distribution. Therefore, the objective of this commentary is to propose an alternative test, with higher statistical power. Likewise, after a brief review of the literature, we make suggestions to students and junior researchers which may prove useful tools for their work (see figure 1).

Figure 1 Recommendation levels and test comparison to estimate data distribution normality 

It has been suggested that for sample sizes >50, the Kolmogorov-Smirnov test (K-S) is recommended; alterantively, for n<50, Shapiro-Will is preferred. These ideas have generally been widely spread through the use of computer statistical packages (e.g., SPPS). However, computer simulations with different sample sizes call for a reconsideration of this belief. Which test has more power? What does the evidence say?

Studies seem to agree that the most recommended test to validate the assumption of normality is Shapiro-Will, while the wide use of the Kolmogorov-Smirnov test should be reconsidered. In conclusion, Shapiro-Will seems to be more sensitive in terms of the normality test. However, with small sample sizes, its interpretation must be assumed with caution ^1-5.

WHAT SOFTWARE TO USE?

There are different statistical packages available to researchers. For example, from licensed software (e.g., STATA, SPSS), to open access (Jamovi, JASP, Rstudio). To run the Shapiro-Wilk test we recommend JASP^{® (6}. It is a free, flexible, and friendly access software. It is constantly updated and in sync with the R programming language. In addition, for frequentist statistical analyses, it has a main emphasis on Bayesian analysis. Figure 2 shows the estimation of the normality test using Shapiro-Wilk through JASP®.

Figure 2 Steps for Shapiro-Wilk Estimation Using JASP^®