Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-17T16:37:39.486Z Has data issue: false hasContentIssue false
  • English
  • Français

Power and Negative Results

Published online by Cambridge University Press:  01 January 2022

Edouard Machery
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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