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Abstract Despite frequent calls for the overhaul of null hypothesis significance testing NHST , this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Keywords: test of significance, test of statistical hypotheses, null hypothesis significance testing, statistical education, teaching statistics, NHST, Fisher, Neyman-Pearson. Introduction This paper introduces the classic approaches for testing research data: tests of significance, which Fisher helped develop and promote starting in ; tests of statistical hypotheses, developed by Neyman and Pearson ; and null hypothesis significance testing NHST , first concocted by Lindquist Fisher's approach to data testing Ronald Aylmer Fisher was the main force behind tests of significance Neyman, and can be considered the most influential figure in the current approach to testing research data Hubbard, Among things to consider when setting the null hypothesis is its directionality.

Open in a separate window. Figure 1. Highlights of Fisher's approach Flexibility. Neyman-Pearson's approach to data testing Jerzy Neyman and Egon Sharpe Pearson tried to improve Fisher's procedure Fisher, ; Pearson, ; Jones and Tukey, ; Macdonald, and ended up developing an alternative approach to data testing.

A priori steps Step 1—Set up the expected effect size in the population. Figure 2.

Figure 3. Figure 4. Figure 5. A posteriori steps Step 7—Calculate the test value for the research RV test. Highlights of Neyman-Pearson's approach More powerful. Null hypothesis significance testing NHST is the most common procedure used for testing data nowadays, albeit under the false assumption of testing substantive hypotheses Carver, ; Nickerson, ; Hubbard, ; Hager, Replication needed, meta-analysis is useful.

Repeated sampling of same population needed, Monte Carlo is useful. NHST None results taken as definitive, especially if significant ; further studies may be sometimes recommended especially if results are not significant. Conflict of interest statement The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References American Psychological Association. The test of significance in psychological research. Controlling the false discovery rate: a practical and powerful approach to multiple testing.

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B Stat. Could Fisher, Jeffreys and Neyman have agreed on testing? The case against statistical significance testing. The case against statistical significance testing, revisited. Testing Fisher, Neyman, Pearson, and Bayes. On the logic and purpose of significance testing.

Methods 2 , — The new statistics: why and how. Using Bayes to get the most out of non-significant results. Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods.

## Fisher, Neyman, and the Creation of Classical Statistics by Erich Lehmann (2011, Paperback)

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Toward evidence-based medical statistics.

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On statistical testing in psychology. The incompleteness of probability models and the resultant implications for theories of statistical inference.

## Book Review: Fisher, Neyman, and the Creation of Classical Statistics

Neyman J. On the problem of confidence intervals. Basic ideas and some recent results of the theory of testing statistical hypotheses. First Course in Probability and Statistics. The problem of inductive inference. Pure Appl.

III , 13—46 Note on an article by Sir Ronald Fisher. Fisher : an appreciation. On the use and interpretation of certain test criteria for purposes of statistical inference: part I.

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## Erich Lehmann’s Birthday: Neyman Pearson vs Fisher on P-values | Error Statistics Philosophy

Biometrika 20A , — On the problem of the most efficient tests of statistical hypotheses. A , — Null hypothesis significance testing: a review of an old and continuing controversy. The place of statistics in psychology. How can significance tests be deinstitutionalized? Methods 15 , — Statistical concepts in the relation to reality.

Oxford: Claredon Press.

A reconceptualization of significance testing. Confidence intervals and tests are two sides of the same research question. Meta-Analytic Procedures for Social Research. Beverly Hills, CA: Sage. Statistical procedures and the justification of knowledge in psychological science. Nonparametric statistics. Statistical significance testing and cumulative knowledge in psychology: implications for training of researchers.

A refutation of the Neyman-Pearson theory of testing.