The crisis of replication has led many to blame statistical significance tests for making it too easy to find impressive looking effects that do not replicate. However, the very fact it becomes difficult to replicate effects when features of the tests are tied down actually serves to vindicate statistical significance tests. While statistical significance tests, used correctly, serve to bound the probabilities of erroneous interpretations of data, this error control is nullified by data-dredging, multiple testing, and other biasing selection effects. Arguments claiming to vitiate statistical significance tests attack straw person variants of tests that commit well-known fallacies and misinterpretations. There is a tension between popular calls for preregistration – arguably, one of the most promising ways to boost replication – and accounts that downplay error probabilities: Bayes Factors, Bayesian posteriors, likelihood ratios. By underscoring the importance of error control for well testedness, the replication crisis points to reformulating tests so as to avoid fallacies and report the extent of discrepancies that are and are not indicated with severity.

复制危机导致许多人指责统计显着性测试，因为它们太容易找到无法复制的令人印象深刻的效果。但是，当测试的功能受到限制时，很难复制效果这一事实实际上证明了统计显着性测试的正确性。尽管正确使用了统计显着性检验可以限制错误数据解释的可能性，但是通过数据挖掘，多次测试和其他有偏见的选择效果，可以消除这种错误控制。声称破坏统计显着性检验的论点攻击了犯有众所周知的谬论和误解的稻草人变异测试。在进行预注册的普遍呼吁之间存在紧张关系-可以说，增强复制的最有前途的方法之一-并说明低估错误概率的原因：贝叶斯因子，贝叶斯后验，似然比。通过强调错误控制对良好测试的重要性，复制危机指出了重新制定测试的方法，以避免出现谬误，并报告严重性和非严重性之间的差异程度。