Tutorial M1: Dienes

Friday July 12 09:00-12:00

M1: Using Bayes to interpret non-significant results

Zoltan Dienes (School of Psychology, University of Sussex, Brighton, U.K.)

The purpose of the tutorial is to present simple tools for dealing with non-significant results. In particular, people will be taught how to apply Bayes Factors to draw meaningful inferences from non-significant data, using free easy-to-use on-line software: Software which allows one to determine whether there is strong evidence for the null and against one’s theory, or if the data are just insensitive, a distinction p-values cannot make. These tools have greater flexibility than power calculations and allow null results to be interpreted over a wider range of situations. Such tools should allow the publication of null results to become easier.

While the tools will be of interest to all scientists, they are especially relevant to researchers interested in the conscious/unconscious distinction, because inferring a mental state is unconscious often rests on affirming a null result. For example, for perception to be below an objective threshold, discrimination about stimulus properties must be at chance. Similarly, for perception to be below a subjective threshold by the zero correla- tion criterion, ability to discriminate one’s own accuracy must be at chance. To interpret a non-significant result, what is needed is a non-arbitrary specification of the distribution of discrimination abilities given conscious knowledge. Conventional statistics cannot solve this problem, but Bayes Factors provide an easy simple solution. The solution is vital for progress in the field, as so many conclusions of unconscious mental states rely on null results with no indication of whether the non-significant result is purely due to data insensitivity.