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This chapter demystifies P-values, hypothesis tests and significance tests,and introduces the concepts of local evidence and global error rates. The localevidence is embodied in \textit{this} data and concerns the hypotheses ofinterest for \textit{this} experiment, whereas the global error rate is aproperty of the statistical analysis and sampling procedure. It is shown usingsimple examples that local evidence and global error rates can be, and shouldbe, considered together when making inferences. Power analysis for experimentaldesign for hypothesis testing are explained, along with the more locallyfocussed expected P-values. Issues relating to multiple testing, HARKing, andP-hacking are explained, and it is shown that, in many situation, their effectson local evidence and global error rates are in conflict, a conflict that canalways be overcome by a fresh dataset from replication of key experiments.Statistics is complicated, and so is science. There is no singular right way todo either, and universally acceptable compromises may not exist. Statisticsoffers a wide array of tools for assisting with scientific inference bycalibrating uncertainty, but statistical inference is not a substitute forscientific inference. P-values are useful indices of evidence and deserve theirplace in the statistical toolbox of basic pharmacologists.

A reckless guide to P-values: local evidence, global errors