shirgall Posted March 7, 2016 Posted March 7, 2016 http://retractionwatch.com/2016/03/07/were-using-a-common-statistical-test-all-wrong-statisticians-want-to-fix-that/ After reading too many papers that either are not reproducible or contain statistical errors (or both), the American Statistical Association (ASA) has been roused to action. Today the group released six principles for the use and interpretation of p values. P-values are used to search for differences between groups or treatments, to evaluate relationships between variables of interest, and for many other purposes. But the ASA says they are widely misused. Here are the six principles from the ASA statement: P-values can indicate how incompatible the data are with a specified statistical model. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. Proper inference requires full reporting and transparency. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.
Cuffy_Meigs Posted March 7, 2016 Posted March 7, 2016 Although it may be inferred from points 1-6, perhaps a seventh in big upper case reminding that they do not imply causality would also be useful as this tends to be conveniently 'forgotten' on occasions.
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