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there is ‘no difference’ or ‘no association’ just because a P value is larger than a threshold such as 0.05 or, equivalently, because a confidence interval includes zero.”2 Instead, the authors suggested using confidence intervals as “compatibility intervals”, that is, your point estimate and confidence interval are the most compatible with your data, given the statistical model you have used to calculate your results.2 Going back to the RCT described above, you conduct your inferential statistical test, and your result is a relative risk of 2.0, with a 95% confidence interval of 1.5–2.5 and a p value far below 0.001. Under traditional statistical test reporting, you would conclude that the people in your intervention group were twice as likely as your control group to be adherent to their medication therapy at 90 days after discharge, and that this difference is statistically significant because the confidence interval does not encompass the measure of equivalence of 1. However, if you were to use the proposal set out by Amrhein and others,2 you would instead state that the values for relative risk, 95% confidence interval, and p value most compatible with your data indicate that people who received your intervention were twice as likely to be adherent to their medication 90 days after hospital discharge, and that the risk difference between your treatment and control groups ranged from 1.5 times more likely to 2.5 times more likely to be adherent, given the assumptions of the statistical testing. In rebuttal to the editorial by Amrhein and others,2 Ioannidis published an editorial the following month in JAMA, entitled “The importance of predefined rules and prespecified statistical analyses: do not abandon significance”.3 In his editorial, Ioannidis emphasized that decisions made in medicine are most often dichotomous, and that more focus is being put on inappropriate claims of finding no statistical difference than on addressing “unwarranted claims of ‘difference’ and unwarranted denial of refutation”, particularly when prespecified rules of statistical testing are not developed or followed by researchers.3 Instead of banning the concept of statistical significance, Ioannidis emphasized that researchers must focus on both following the rules of statistical testing and ensuring that clinical relevance is applied to decision-making.3 It is clear that underEDITORIAL

Do We Give Too Much Significance to Statistical Significance?