Time to dispense with the p-value in OR?

Marko Hofmann () and Silja Meyer-Nieberg ()
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Marko Hofmann: Universität der Bundeswehr München
Silja Meyer-Nieberg: Universität der Bundeswehr München

Central European Journal of Operations Research, 2018, vol. 26, issue 1, No 9, 193-214

Abstract: Abstract Null hypothesis significance testing is the standard procedure of statistical decision making, and p-values are the most widespread decision criteria of inferential statistics both in science, in general, and also in operations research, in particular. p-values are of paramount importance in the life and human sciences, and dominate statistical summaries in natural and technical sciences as well as in operations research, a domain in which the p-value seems to be a common denominator for decision making based on samples. Yet, the use of significance testing in the analysis of research data has been criticized from numerous statisticians—continuously for almost 100 years. This criticism has recently (March 7, 2016) been given an official status by a statement from the American Statistical Association on p-values. Is it time to dispense with the p-value in OR? The answer depends on many factors, including the research objective, the research domain, and, especially, the amount of information provided in addition to the p-value. Despite this dependence from context three conclusions can be made that should concern the operational analyst: First, p-values can perfectly cast doubt on a null hypothesis or its underlying assumptions, but they are only a first step of analysis, which, stand alone, lacks expressive power. Second, the statistical layman almost inescapably misinterprets the evidentiary value of p-values. Third and foremost, p-values are an inadequate choice for a succinct executive summary of statistical evidence for or against a research question. In statistical summaries confidence intervals of standardized effect sizes provide much more information than p-values without requiring much more space.

Keywords: Statistical decision making; Inferential statistics; Statistical evidence; p-values; Aggregation of statistical information; Effect sizes; Confidence intervals (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10100-017-0484-9

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