To get evidence for or against a theory relative to the null hypothesis, one needs to know what the theory predicts. The amount of evidence can then be quantified by a Bayes factor. Specifying the sizes of the effect one’s theory predicts may not come naturally, but I show some ways of thinking about the problem, some simple heuristics that are often useful when one has little relevant prior information. These heuristics include the room-to-move heuristic (for comparing mean differences), the ratio-of-scales heuristic (for regression slopes), the ratio-of-means heuristic (for regression slopes), the basic-effect heuristic (for analysis of variance effects), and the total-effect heuristic (for mediation analysis).