Statistical power analysis in wildlife research

Robert J. Steidl, John P. Hayes, Eric Schauber

Research output: Contribution to journalArticlepeer-review

309 Scopus citations


Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be ≤0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect size hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency. (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to be reported, then the β-level, effect sizes, and sample sizes used in calculation must also be reported.

Original languageEnglish (US)
Pages (from-to)270-279
Number of pages10
JournalJournal of Wildlife Management
Issue number2
StatePublished - Apr 1997


  • Confidence intervals
  • Effect size
  • Experimental design
  • Hypothesis testing
  • Power
  • Research design
  • Sample size
  • Statistical inference
  • Statistical power analysis
  • Type I error
  • Type II error

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Nature and Landscape Conservation


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