This article compares 14 methods for testing the statistical significance of intervening variable effects, which are crucial in various disciplines such as psychology, sociology, and epidemiology. The authors conducted a Monte Carlo study to evaluate the Type I error rates and statistical power of these methods. The commonly used Baron and Kenny (1986) approach was found to have low statistical power and Type I error rates. Two difference-in-coefficients methods and two methods based on the distribution of the product were identified as having the most accurate Type I error rates and greatest statistical power, except in one case where Type I error rates were too high. The best balance of Type I error and statistical power across all cases was found to be the test of the joint significance of the two effects comprising the intervening variable effect. The article also discusses the conceptual issues related to the definition of intervening variable effects and provides recommendations for researchers.This article compares 14 methods for testing the statistical significance of intervening variable effects, which are crucial in various disciplines such as psychology, sociology, and epidemiology. The authors conducted a Monte Carlo study to evaluate the Type I error rates and statistical power of these methods. The commonly used Baron and Kenny (1986) approach was found to have low statistical power and Type I error rates. Two difference-in-coefficients methods and two methods based on the distribution of the product were identified as having the most accurate Type I error rates and greatest statistical power, except in one case where Type I error rates were too high. The best balance of Type I error and statistical power across all cases was found to be the test of the joint significance of the two effects comprising the intervening variable effect. The article also discusses the conceptual issues related to the definition of intervening variable effects and provides recommendations for researchers.