The p-Value Requires Context, Not a Threshold
By Rebecca A. Betensky. American Statistician, 2019
160 citations recorded by the source corpus.
Abstract It is widely recognized by statisticians, though not as widely by other researchers, that the p-value cannot be interpreted in isolation, but rather must be considered in the context of certain features of the design and substantive application, such as sample size and meaningful effect size. I consider the setting of the normal mean and highlight the information contained in the p-value in conjunction with the sample size and meaningful effect size. The p-value and sample size jointly yield 95% confidence bounds for the effect of interest, which can be compared to the predetermined meaningful effect size to make inferences about the true effect. I provide simple examples to demonstrate that although the p-value is calculated under the null hypothesis, and thus seemingly may be divorced from the features of the study from which it arises, its interpretation as a measure of evidence requires its contextualization within the study. This implies that any proposal for improved use of the p-value as a measure of the strength of evidence cannot simply be a change to the threshold for significance.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/46ae3e03b242f5bd5b00c66a66a7cf2c4d0233c0
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Citations in the corpus
- Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “” When discussing the statistical significance of the hypothesis tests, the reporting of the exact p values and interpreting them in the focal context is recommended instead of focusing on some particular cut- offs (Betensky, 2019; Wasserstein et al., 2019).”
- Moving to a World Beyond “p < 0.05” (2019, unknown): “Billheimer (2019) suggests abandoning inference about parameters, which are frequently hypothetical quantities used to idealize a problem.”
- Reconnecting p-Value and Posterior Probability Under One- and Two-Sided Tests (2018, methodology): “Focusing on discussions surrounding p-values, a special issue of The American Statistician (2019) contains many proposals to adjust, abandon or provide alternatives to p-value (e.g., Benjamin and Berger 2019; Betensky 2019; Billheimer 2019; Manski 2019; Matthews 2019, among others).”