Toward that end, we attempt to explain the meaning of significance tests, confidence intervals, and statistical power in a more general and critical way than is traditionally done, and then review 25 common misconceptions in light of our explanations. We thus think it imperative that basic teaching as well as general understanding of these methods be improved. ĭespite such bans, we expect that the statistical methods at issue will be with us for many years to come.
One journal now bans all statistical tests and mathematically related procedures such as confidence intervals, which has led to considerable discussion and debate about the merits of such bans. Misinterpretation and abuse of statistical tests has been decried for decades, yet remains so rampant that some scientific journals discourage use of “statistical significance” (classifying results as “significant” or not based on a P value).
We conclude with guidelines for improving statistical interpretation and reporting. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations.
In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature.
Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant.
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