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Meehl, P.E. (1997) The Problem Is Epistemology, Not Statistics: Replace Significance Tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions. In: Harlow, L., Mulaik, S.A. and Steiger, J.H., Eds., What If There Were No Significance Tests? Erlbaum, Mahwah, NJ, 393-425.

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• TITLE: Why It Is Problematic to Calculate Probabilities of Findings Given Range Null Hypotheses

  AUTHORS: David Trafimow

  KEYWORDS: Range Hypotheses, One-Tailed Test, Range Null Hypothesis, Uninformative Hypotheses

  JOURNAL NAME: Open Journal of Statistics, Vol.7 No.3, June 20, 2017

  ABSTRACT: An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.