Abstract
In the context of hypothesis testing simulation studies, this paper advocates using graphical and numerical tools to summarize the results, beyond the conventional practice of just reporting empirical levels. Some of these tools are defined by us. They are mainly based on the computation of distances between the empirical distribution function of the p-values derived from the simulation experiment and the distribution function of the U([0, 1]) random variable. Their null distribution is tabulated. A joint study of several distances reveals that important aspects of a test can pass unnoticed if only empirical significance levels are calculated. The proposed tools are applied to two practical examples, demonstrating their usefulness in the discrimination between alternative test procedures and also in the detection of data not according to the null hypothesis.
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