Significance Tests are Not Enough

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Abstract
Chow (1991) distinguishes between `practical impact' and `conceptual rigor' research, and he concludes that effect-size estimation is useful only in practical impact research. I argue that significance tests do not answer substantive questions about the data and are useful only as a check that the results are unlikely to have occurred by chance. Chow's decision to regard the similarity between data and prediction as being a dichotomous judgment made on the basis of significance testing is therefore unwise. I conclude that effect sizes are the single best index of the relationship between theoretical predictions and the obtained data. The role of replications and meta-analysis in advancing theory is also discussed.