Using Bayes to get the most out of null results

User's of orthodox statistics, including psychologists, have typically not interpreted null results in a principled way, resulting in mistaken conclusions and choices of research direction based on non-significant findings. A non-significant result does not distinguish no evidence for an effect from evidence for no effect, radically different states of affairs. The distinction falls out naturally from a Bayesian analysis, in a consistent way that cannot be accomplished with orthodox statistics, even when statistical power is taken into account. I will show how simple free online software for calculating Bayes Factors can be used to determine what a null result is actually telling us. The new tools I introduce in this talk, and now gradually appearing in the literature, should enable publishing null results on an equal footing with significant results. I hope to make people aware of a genuine practical hole in current practice in any domain of psychology and offer a genuine practical solution. The talk will be useful to anyone who uses inferential statistics. No statistical background is assumed other than knowledge of how to do a t-test.