Full list of author information is available at the end of the article Use of p-values is widespread in the sciences, especially so in biomedical research, and also underlies several analytic approaches in data mining. Its original intent is simple enough, but its application and interpretation are far from simple. If data is collected to evaluate an idea, a hypothesis, then accepting the idea, when it is true, is a good thing, and rejecting the idea when it is not true, is also good. Two errors in reasoning from the data then can occur: a true idea is rejected (making a Type I error), or a false idea is accepted (Type II error). However, simple criticisms and essential distinctions are immediate: (1) The p-value is not a probability of an idea being true; such a more evolved statement requires using Bayes theorem—at least—and a different frame for inference; (2) Just stating the result of a statistical test as a p-value is nearly uninformative, as a statistically significant outcome may have no practical biological importance; and continuing (3) The size of the departure from the proposed true idea, the effect size, could be quite small in th
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