The case against retrospective statistical power analyses with an introduction to power analysis
Commentary
First Online:
Received:
Revised:
Accepted:
- 842 Downloads
- 54 Citations
Keywords
Statistical power analysis Retrospective power analysis Power approach paradox Effect sizeNotes
Acknowledgements
We gratefully acknowledge James McEwan, Richard Etheredge, Catherine Sumpter, Jens Rolff, and an anonymous referee for comments that have improved the manuscript. S. Nakagawa is supported by Foundation for Research Science and Foundation, New Zealand.
References
- Berger RL, Hsu JC (1996) Bioequivalence trials, intention-union tests and equivalence confidence sets. Stat Sci 11:283–319CrossRefGoogle Scholar
- Carver RP (1978) The case against statistical significance testing. Harv Educ Rev 48:378–399Google Scholar
- Chow SL (1988) Significance test or effect size? Psychol Bull 103:105–110CrossRefGoogle Scholar
- Cohen J (1962) The statistical power of abnormal social psychological research: a review. J Abnorm Soc Psychol 65:145–153PubMedGoogle Scholar
- Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, HillsdaleGoogle Scholar
- Cohen J (1990) Things I have learned (so far). Am Psychol 45:1304–1312CrossRefGoogle Scholar
- Cohen J (1992) Statistical power analysis. Curr Dir Psychol Sci 1:98–101CrossRefGoogle Scholar
- Cohen J (1994) The earth is round (P<0.05). Am Psychol 49:997–1003CrossRefGoogle Scholar
- Colegrave N, Ruxton GD (2003) Confidence intervals are a more useful complement to nonsignificant tests then are power calculations. Behav Ecol 14:446–450Google Scholar
- Dayton PK (1998) Reversal of the burden of proof in fisheries management. Science 279:821–822CrossRefGoogle Scholar
- Fairweather PG (1991) Statistical power and design requirements for environmental monitoring. Aust J Mar Freshwater Res 42:555–567Google Scholar
- Fisher RA (1935) The design of experiments. Hafner, New YorkGoogle Scholar
- Fleiss JL (1994) Measures of effect size for categorical data. In: Cooper H, Hedges LV (eds) The handbook of research synthesis. Sage, New York, pp 245–260Google Scholar
- Frick RW (1995) Accepting the null hypothesis. Mem Cognit 23:132–138PubMedGoogle Scholar
- Gerard PD, Smith DR, Weerkkody G (1998) Limits of retrospective power analysis. J Wildl Manage 62:801–807Google Scholar
- Glass GV (1977) Integrating findings: the meta-analysis of research. In: Shulman L (ed) Review of research in education, vol 5. Peacock, Itasca, pp 351–379Google Scholar
- Goodman SN, Berklin JA (1994) The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Intern Med 121:200–206PubMedGoogle Scholar
- Hayes JP, Steidl RJ (1997) Statistical power analysis and amphibian population trends. Conser Biol 11:273–275CrossRefGoogle Scholar
- Hedges LV (1981) Distributional theory for Glass’s estimator of effect size and related estimators. J Educ Stat 6:107–128Google Scholar
- Hedges L, Olkin I (1985) Statistical methods for meta-analysis. Academic, New YorkGoogle Scholar
- Hoenig JM, Heisey DM (2001) The abuse of power: the pervasive fallacy of power calculations for data analysis. Am Stat 55:19–24CrossRefGoogle Scholar
- Hunter JE, Schmidt FL (1990) Methods of meta-analysis: correcting error and bias in research findings. Sage, Newbury ParkGoogle Scholar
- Jennions MD, Møller AP (2003) A survey of the statistical power of research in behavioral ecology and animal behavior. Behav Ecol 14:438–445Google Scholar
- Kirk RE (1996) Practical significance: a concept whose time has come. Educ Psychol Meas 56:746–759Google Scholar
- Lipsey MW, Wilson DB (2000) Practical meta-analysis. Sage, Beverly HillsGoogle Scholar
- Maddocks SA, Bennett ATD, Hunt S, Cuthill IC (2001) Context-dependent visual preferences in starlings and blue tits: mate choice and light environment. Anim Behav 63:69–75CrossRefGoogle Scholar
- Nakagawa S (2004) A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav Ecol (doi: 10.1093/heheco/arh107) (in press)Google Scholar
- Nickerson RS (2000) Null hypothesis significance testing: a review of an old and continuing controversy. Psychol Methods 5:241–301CrossRefPubMedGoogle Scholar
- Parkhurst DF (2001) Statistical significance tests: equivalence and reverse tests should reduce misinterpretation. BioScience 51:1051–1057Google Scholar
- Perlman M, Wu L (1999) The emperor’s new tests. Stat Sci 14:355–369Google Scholar
- Rosenthal R (1993) Cumulating evidence. In: Keren G, Lewis C (eds) A handbook for data analysis in the behavioral sciences: methodological issues. Erlbaum, Hillsdale, pp 519–559Google Scholar
- Rosenthal R (1994) Parametric measures of effect size. In: Cooper H, Hedges LV (eds) The handbook of research synthesis. Sage, New York, pp 231–244Google Scholar
- Sedlmeier P, Gigerenzer G (1989) Do studies of statistical power have an effect on the power of studies. Psychol Bull 105:309–316CrossRefGoogle Scholar
- Steidl RJ, Thomas L (2001) Power analysis and experimental design. In: Scheiner SM, Gurevitch J (eds) Design and analysis of ecological experiments, 2 edn. Oxford University Press, Oxford, pp 14–36Google Scholar
- Still AW (1992) On the number of subjects used in animal behaviour experiments. Anim Behav 30:873–880Google Scholar
- Stoehr AM (1999) Are significance threshold appropriate for the study of animal behaviour? Anim Behav 57:F22–F25CrossRefPubMedGoogle Scholar
- Thomas L (1997) Retrospective power analysis. Conser Biol 11:276–280CrossRefGoogle Scholar
- Thomas RJ, Cuthill IC (2002) Body mass regulation and the daily singing routines of European robins. Anim Behav 63:285–292CrossRefGoogle Scholar
- Thompson B (2002) What future quantitative social science research could look like: confidence intervals for effect sizes. Educ Res 31:25–32Google Scholar
- Thompson CF, Neill AJ (1991) House wrens do not prefer clean nestboxes. Anim Behav 42:1022–1024Google Scholar
Copyright information
© Springer-Verlag and ISPA 2004