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16 - Sampling and Statistical Power

Published online by Cambridge University Press:  19 September 2019

Joanna M. Setchell
Joanna M. Setchell
Affiliation:
Durham University
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Summary

Good research design includes careful consideration of the number of independent observations (replicates) we need to test our predictions – the sample size. Some sampling decisions are beyond our control. For example, we may be limited by the number of specimens available, the animals we can observe, or the data we have at our disposal. Knowing in advance what we can and can’t test with our data will save wasted effort. This chapter covers how we use samples to study populations, the importance of statistical power, how to determine whether you have the power to test for an effect, and statistical precision.

Keywords

study designresearch designindependent observationsreplicatesdatasample sizepowerType I errorType II errorfalse positivefalse negative
Type
Chapter
Information
Studying Primates
How to Design, Conduct and Report Primatological Research
 , pp. 207 - 214
Publisher: Cambridge University Press
Print publication year: 2019

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References

Further Reading

Hoenig, JM, Heisey, DM. 2001. The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician 55: 19–24. https://doi.org/10.1198/000313001300339897. Shows that post hoc power calculations are common but flawed.CrossRefGoogle Scholar

16.6 Further Reading

Altmann, J. 1974. Observational study of behavior: Sampling methods. Behaviour 49: 227–267. https://doi.org/10.1163/156853974X00534. A paradigmatic example of thinking through the strengths and weaknesses of different sampling regimes.CrossRefGoogle ScholarPubMed
Cumming, G, Calin-Jageman, R. 2012. Understanding the New Statistics: Estimation, Open Science and Beyond. New York: Routledge. Includes precision for planning or accuracy in parameter estimation approaches.Google Scholar
Ellis, PD. 2010. The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results. Cambridge: Cambridge University Press. Written for the social sciences, but just as useful for primatology. Clear, succinct, and jargon-free.Google Scholar
Field, A, Hole, G. 2003. How to Design and Report Experiments. London: Sage Publications Ltd. Covers power on pages 154–157.Google Scholar
Johnson, PCD, Barry, SJE, Ferguson, HM, Müller, P. 2015. Power analysis for generalized linear mixed models in ecology and evolution. Methods in Ecology and Evolution 6: 133–142. https://doi.org/10.1111/2041-210X.12306. A clear discussion of power, and a simulation-based power analysis method appropriate for generalised linear mixed models.Google Scholar
Kelley, K, Maxwell, SE. 2003. Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods 8: 305–321. Introduces accuracy in parameter estimation (AIPE).CrossRefGoogle Scholar
Reinhart, A. 2015. Statistics Done Wrong: The Woefully Complete Guide. San Francisco, CA: No Starch Press. Chapter 2 covers statistical power.Google Scholar

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