Addressing Limitations of p in Counseling Research through Reporting of Bayes Factor Bound and Effect Size Precision

Abstract

Abstract Null hypothesis statistical testing (NHST) in its present form continues to be the predominant method of quantitative research. Bayesian methods, which emphasize the probability of a hypothesis given the data, may be growing in popularity due to the advent of statistical software employing these methods. Rather than discarding the present methods of NHST, the purpose of this article is to enhance understanding of NHST for counseling researchers and provide an augmentation to presenting results that address the known limitations of counseling research and other social science research. Such recommendations include summarizing previous recommendations of employing visuals to explain data, incorporating effect size confidence intervals, and utilizing the Bayes factor bound to supplement p-values and explain the probabilities related to acceptance or rejection of the null hypothesis.