To the Editor: The explanation of inference from confidence intervals by Hemming and Taljaard is interesting but unfortunately incorrect.1 The authors may have fallen for the confidence interval variation of the P value fallacy — the mistaken idea that the P value (or confidence interval) can capture both the long term outcomes of an experiment, as commonly reflected in the phrase “a trend to significance (P = 0.06)”, and the evidential meaning of a single result.2
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- 1. Hemming K, Taljaard M. Why proper understanding of confidence intervals and statistical significance is important. Med J Aust 2021; 214: 116–118. https://www.mja.com.au/journal/2021/214/3/why‐proper‐understanding‐confidence‐intervals‐and‐statistical‐significance
- 2. Goodman SN. Toward evidence‐based medical statistics. 1: The P value fallacy. Ann Intern Med 1999; 130: 995–1004.
- 3. Goodman SN. Toward evidence‐based medical statistics. 2: The Bayes factor. Ann Intern Med 1999; 130: 1005–1013.
- 4. Hurley JC, Bronwridge D. Could simulation methods solve the curse of sparse data within clinical studies of antibiotic resistance? JAC Antimicrob Resist 2021; 3: dlab016.
- 5. Wood J, Freemantle N, King M, Nazareth I. Trap of trends to statistical significance: likelihood of near significant P value becoming more significant with extra data. BMJ 2014; 348: g2215.
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