The P-Value Debate and Implications for Management Research : The Significance of A Priori Probability

This paper argues that the P-value debate is grounded in the confusion between statistical significance testing and statistical inferences. Using results of statistical significance testing to make statistical inferences can lead to the validation of many specious theories. While errors in statistical significance testing refer to the probability of false positives, errors in statistical inferences lie in the inverse probability of false positives. The relationship between two probabilities is highly affected by a priori probability. A higher a priori probability decreases the probability of errors when using results of statistical significance testing for statistical inferences. By contrast, a lower a priori probability increases the probability of statistical inference errors even if the probability of false positives is low. In order to improve the reliability of statistical inferences, this paper draws on management research and proposes to better establish a priori probability through a dual dialogue with practical and theoretical worlds. By doing so, this paper underlines the pervasiveness of underdetermination and sheds light on the interplay of the a priori and the empirical in management research.