{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T13:39:06Z","timestamp":1780407546270,"version":"3.54.1"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Technology"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> It has long been argued that we need to consider much more than an observed point estimate and a p-value to understand statistical results. One of the most persistent misconceptions about p-values is that they are necessarily calculated assuming a null hypothesis of no effect is true. Instead, p-values can and should be calculated for multiple hypothesized values for the effect size. For example, a p-value function allows us to visualize results continuously by examining how the p-value varies as we move across possible effect sizes. For more focused discussions, a 95% confidence interval shows the subset of possible effect sizes that have p-values larger than 0.05 as calculated from the same data and the same background statistical assumptions. In this sense a confidence interval can be taken as showing the effect sizes that are most compatible with the data, given the assumptions, and thus may be better termed a compatibility interval. 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