The practical alternative to the p-value is the correctly used p-value

Due to the strong overreliance on p-values in the scientific literature some researchers have argued that p-values should be abandoned or banned, and that we need to move beyond p-values and embrace practical alternatives. When proposing alternatives to p-values statisticians often commit the ‘Statistician’s Fallacy’, where they declare which statistic researchers really ‘want to know’. Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. In some situations, the answer to the question they are most interested in will be the p-value. As long as null-hypothesis tests have been criticized, researchers have suggested to include minimum-effect tests and equivalence tests in our statistical toolbox, and these tests (even though they return p-values) have the potential to greatly improve the questions researchers ask. It is clear there is room for improvement in how we teach p-values. If anyone really believes p-values are an important cause of problems in science, preventing the misinterpretation of p-values by developing better evidence-based education and user-centered statistical software should be a top priority. Telling researchers which statistic they should use has distracted us from examining more important questions, such as asking researchers what they want to know when they do scientific research. Before we can improve our statistical inferences, we need to improve our statistical questions.