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Power and Negative Results

Published online by Cambridge University Press:  01 January 2022

Edouard Machery
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Abstract

The use of power to infer null hypotheses from negative results has recently come under severe attack. In this article, I show that the power of a test can justify accepting the null hypothesis. This argument also gives us a new powerful reason for not treating p-values and power as measures of the strength of evidence.

Type
Inference and Statistics
Information
Philosophy of Science , Volume 79 , Issue 5 , December 2012 , pp. 808 - 820
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

I would like to thank Greg Gandenberger for his comments on a previous version of this article.

References

Boroditsky, Lera. 2001. “Does Language Shape Thought? Mandarin and English Speakers’ Conceptions of Time.” Cognitive Psychology 43:1–22.CrossRefGoogle ScholarPubMed
Chen, Jenn-Yeu. 2007. “Do Chinese and English Speakers Think about Time Differently? Failure of Replicating Boroditsky (2001).” Cognition 104:427–36.CrossRefGoogle Scholar
Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Erlbaum.Google Scholar
Cornfield, Jerome. 1966. “Sequential Trials, Sequential Analysis, and the Likelihood Principle.” American Statistician 20:18–23.Google Scholar
Fidler, Fiona. 2002. “The Fifth Edition of the APA Publication Manual: Why Its Statistics Recommendations Are So Controversial.” Educational and Psychological Measurement 62:749–70.CrossRefGoogle Scholar
Hare, Brian, Melis, Alicia, Woods, Patricia, Hastings, Sara, and Wrangham, Richard. 2007. “Tolerance Allows Bonobos to Outperform Chimpanzees on a Cooperative Task.” Current Biology 17:619–23.CrossRefGoogle ScholarPubMed
Hoenig, John M., and Heisey, Dennis M.. 2001. “The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis.” Statistical Practice 55:1–6.Google Scholar
January, David, and Kako, Edward. 2007. “Re-evaluating the Evidence for Linguistic Relativity: Reply to Boroditsky (2001).” Cognition 104:417–26.CrossRefGoogle Scholar
Kempthorne, Oscar, and Folks, Leroy. 1971. Probability, Statistics, and Data Analysis. Ames: Iowa State University Press.Google Scholar
Lenth, R. V. 2007. “Statistical Power Calculations.” Journal of Animal Science 85:E24–E29.CrossRefGoogle ScholarPubMed
Leventhal, L. 2009. “Statistical Power Calculations: Comment.” Journal of Animal Science 87:1854–55.CrossRefGoogle ScholarPubMed
Machery, Edouard. 2012. “Evidence and Cognition.” Unpublished manuscript, University of Pittsburgh.Google Scholar
Oakes, Michael. 1986. Statistical Inference: A Commentary for the Social and Behavioral Sciences. New York: Wiley.Google Scholar
Poitevineau, Jacques, and Lecoutre, Bruno. 2001. “Interpretation of Significance Levels by Psychological Researchers: The .05 Cliff Effect May Be Overstated.” Psychonomic Bulletin and Review 8:847–50.CrossRefGoogle ScholarPubMed
Rosenthal, Robert, and Gaito, John. 1963. “The Interpretation of Levels of Significance by Psychological Researchers.” Journal of Psychology 55:33–38.CrossRefGoogle Scholar
Royall, Richard M. 1986. “The Effect of Sample Size on the Meaning of Significance Tests.” American Statistician 40:313–15.Google Scholar
Royall, Richard M.. 1997. Statistical Evidence: A Likelihood Paradigm. New York: Chapman & Hall.Google Scholar
Sedlmeier, Peter, and Gigerenzer, Gerd. 1989. “Do Studies of Statistical Power Have an Effect on the Power of Studies?” Psychological Bulletin 105:309–16.CrossRefGoogle Scholar
Wilkinson, Leland, and the Task Force on Statistical Inference. 1999. “Statistical Methods in Psychology Journals: Guidelines and Explanations.” American Psychologist 54:594–604.CrossRefGoogle Scholar