Using Bayes to Interpret Non-significant Results


The purpose of the tutorial is to present simple tools for dealing with non-significant results, an area which cognitive scientists have consistently found problematic. In particular, people will be taught how to apply Bayes Factors and likelihood intervals 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.

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