Advice: Don't automate the decision to use a nonparametric test

It sounds so simple. First perform a normality test. If the P value is low, demonstrating that the data do not follow a Gaussian distribution, choose a nonparametric test. Otherwise choose a conventional test.

Prism does not follow this approach, because the choice of parametric vs. nonparametric is more complicated than that.

•	Often, the analysis will be one of a series of experiments. Since you want to analyze all the experiments the same way, you cannot rely on the results from a single normality test.

•	If data deviate significantly from a Gaussian distribution, you should consider transforming the data to create a Gaussian distribution. Transforming to reciprocals or logarithms are often helpful.

•	Data can fail a normality test because of the presence of an outlier.

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The decision of whether to use a parametric or nonparametric test is most important with small data sets (since the power of nonparametric tests is so low). But with small data sets, normality tests have little power to detect nongaussian distributions, so an automatic approach would give you false confidence.

The decision of when to use a parametric test and when to use a nonparametric test is a difficult one, requiring thinking and perspective. This decision should not be automated.