Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing, misinterpretation, and alternatives. Statistical significance testing requires subjective judgment in setting a predetermined acceptable probability of making an inferential error caused by the sampling error. Sampling error can only be eliminated by gathering data from the entire population. Statistical significance addresses the question of whether, assuming the sample data came from a population in which the null hypothesis is (exactly) true, the calculated probability of the sample results is less than the acceptable limit imposed regarding a Type I error. Reasons not to use statistical significance testing and questions of misinterpretation are reviewed. Two analyses that should be emphasized over statistical significance testing are effect sizes and the empirical replicability of results. (Contains 6 references.) (SLD)