Chapter 69 Power Analysis

Both Type I Errors (e.g., false positives) and Type II Errors (e.g., false negatives) should be of concern when designing an organizational study and when interpreting the results of statistical findings. A power analysis can be conducted to design studies (e.g., appropriate sample size) that will be able to detect an effect (e.g., difference, association) of a particular magnitude while also attempting to mitigate false positives. Somewhat controversially, power analyses can also be conducted after the data have been collected in order to determine whether the design yielded sufficient power to detect an effect that truly exists in the underlying population; for more information on this controversy check out the following article:

Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19-24.

In the video that follows, I provide a brief overview of power analysis.

Link to conceptual video: