The insignificance of statistical significance testing
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Abstract
Despite their use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of P-values, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | The insignificance of statistical significance testing |
| Series title | Journal of Wildlife Management |
| DOI | 10.2307/3802789 |
| Volume | 63 |
| Issue | 3 |
| Year Published | 1999 |
| Language | English |
| Publisher | Wildlife Soceity |
| Contributing office(s) | Northern Prairie Wildlife Research Center |
| Description | 10 p. |
| First page | 763 |
| Last page | 772 |
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