Three strategies for augmenting the interpretation of significance test results are illustrated. Determining the most suitable indices to use in evaluating empirical results is a matter of considerable debate among researchers. Researchers increasingly recognize that significance tests are very limited in their potential to inform the interpretation of scientific results. The first strategy involves evaluating significance test results in a sample size context. The researcher is encouraged to determine at what smaller sample size a statistically significant fixed effect size would no longer be significant, or conversely, at what larger sample size a non-significant result would become statistically significant. The second strategy would involve interpreting effect size as an index of result importance. The third strategy emphasizes interpretation based on the estimated likelihood that results will replicate. These applications are illustrated via small heuristic data sets to make the discussion more concrete. A 37-item list of references, seven data tables, and an appendix illustrating relevant computer commands are provided. (TJH)