**Presentation summary** It is not possible to assess the evidence for a null effect when using traditional frequentist inference, regardless of the size of the p-value. Thus, non-significant effects are not terribly informative, as these p-values either indicate support for a null effect or insensitive data. This video provides a non-technical introduction to Bayesian hypothesis testing, which can assess the relative evidence for a null or alternative hypothesis. I walk through how to perform number of Bayesian equivalents to common frequentist p-value tests, such as correlations, t-tests, and ANOVAs. I will also introduce equivalence tests, which were originally popularised in the pharmacokinetics field and have been described as a “reversed t-tests”, provide a means to examine whether you can reject the presence of a smallest effect size of interest (SESOI) within a frequentist p-value framework. Bayesian hypothesis tests and equivalence tests are not only useful tools for theory falsification when designing studies, but also convenient for revisiting non-significant results that have already been published. ---------- **Presentation video** @[youtube](https://youtu.be/Ma1G4-aKR9A)