Choosing alpha post hoc: the danger of multiple standard significance thresholds

Abstract: A fundamental assumption of classical hypothesis testing is that the significance threshold $\alpha$ is chosen independently from the data. The validity of confidence intervals likewise relies on choosing $\alpha$ beforehand. We point out that the independence of $\alpha$ is guaranteed in practice because, in most fields, there exists one standard $\alpha$ that everyone uses -- so that $\alpha$ is automatically independent of everything. However, there have been recent calls to decrease $\alpha$ from $0.05$ to $0.005$. We note that this may lead to multiple accepted standard thresholds within one scientific field. For example, different journals may require different significance thresholds. As a consequence, some researchers may be tempted to conveniently choose their $\alpha$ based on their p-value. We use examples to illustrate that this severely invalidates hypothesis tests, and mention some potential solutions.