Abstract
P-values combined with estimates of effect size are used to assess the importance of experimental results. However, their interpretation can be invalidated by selection bias when testing multiple hypotheses, fitting multiple models or even informally selecting results that seem interesting after observing the data. We offer an introduction to principled uses of p-values (targeted at the non-specialist) and identify questionable practices to be avoided.
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