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The replication crisis has seen increased focus on best practice techniques to improve the reliability of scientific findings. What remains elusive to many researchers and is frequently misunderstood is that predictions involving interactions dramatically affect the calculation of statistical power. Using recent papers published in Personality and Social Psychology Bulletin (PSPB), we illustrate the pitfalls of improper power estimations in studies where attenuated interactions are predicted. Our investigation shows why even a programmatic series of six studies employing 2 × 2 designs, with samples exceeding N = 500, can be woefully underpowered to detect genuine effects. We also highlight the importance of accounting for error-prone measures when estimating effect sizes and calculating power, explaining why even positive results can mislead when power is low. We then provide five guidelines for researchers to avoid these pitfalls, including cautioning against the heuristic that a series of underpowered studies approximates the credibility of one well-powered study.
Blake et al. (Wed,) studied this question.