Quality (philosophy)

Null hypothesis

Philosophy

Toolbox

Statistics

Economics

Computer science

Ask price

Statistic

Database

Psychology

Mathematics

Statistical hypothesis testing

Commit

Value (mathematics)

Fallacy

Epistemology

Alternative hypothesis

Economy

Programming language

Statistician

p-value

Machine learning

Perspectives on Psychological Science

Because of the strong overreliance on p values in the scientific literature, some researchers have argued 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,” whereby 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 including minimum-effect tests and equivalence tests in our statistical toolbox, and these tests have the potential to greatly improve the questions researchers ask. If anyone believes p values affect the quality of scientific research, preventing the misinterpretation of p values by developing better evidence-based education and user-centered statistical software should be a top priority. Polarized discussions about which statistic scientists should use has distracted us from examining more important questions, such as asking researchers what they want to know when they conduct scientific research. Before we can improve our statistical inferences, we need to improve our statistical questions.

The Practical Alternative to the <i>p</i> Value Is the Correctly Used <i>p</i> Value

The Practical Alternative to the p Value Is the Correctly Used p Value