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5 - Understanding Statistical Evidence

Published online by Cambridge University Press:  19 September 2019

Joanna M. Setchell
Affiliation:
Durham University
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Summary

Statistical evidence is fundamental to science. Understanding statistics helps us to understand the literature and assess it critically, refine our research questions into testable hypotheses and predictions, design studies that are appropriate to test these predictions, evaluate whether our findings support our predictions, and derive appropriate conclusions. The dominant paradigm in primatology and allied disciplines is to test whether patterns we observe in our observations are due to more than random variation in our data. However, the statistical analyses we use to do this are very often misinterpreted. In this chapter I distinguish different kinds of variables, then introduce relationships between variables. I explain how we use statistical analysis to infer something about a theoretical population based on a sample. I introduce null hypothesis significance testing and explain common misunderstandings of this approach. I review the two types of error that arise in NHST and the concept of statistical power. I explain the need to assess and report effect sizes and confidence intervals, briefly introduce alternatives to null hypothesis statistical testing and end with how to interpret statistical results appropriately.

Type
Chapter
Information
Studying Primates
How to Design, Conduct and Report Primatological Research
, pp. 53 - 66
Publisher: Cambridge University Press
Print publication year: 2019

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References

Cumming, G, Calin-Jageman, R. 2012. Understanding the New Statistics: Estimation, Open Science, and Beyond. New York: Routledge. Covers how null hypothesis statistical testing leads to dichotomous thinking and the advantages of estimation based on effect sizes, CIs, and meta-analysis. ‘New’ in the title means ‘new to most researchers’ rather than that the methods are new.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. A simple guide to reporting and interpreting effect sizes. Written for the social sciences, but just as useful for primatology.CrossRefGoogle Scholar
Greenland, S, Senn, SJ, Rothman, KJ, Carlin, JB, Poole, C, Goodman, SN, Altman, DG. 2016. Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology 31: 337350. https://doi.org/10.1007/s10654–016-0149-3. A very useful explanation of the misinterpretation of statistical tests, p values, and confidence intervals.CrossRefGoogle ScholarPubMed
Loftus, GR, Masson, MEJ. 1994. Using confidence intervals within subject designs. Psychonomic Bulletin & Review 1: 476490. https://doi.org/10.3758/bf03210951. Begins with the history of hypothesis-testing and goes on to review the use of CIs in figures to help the reader assess patterns in the data.CrossRefGoogle ScholarPubMed
Reinhart, A. 2015. Statistics Done Wrong: The Woefully Complete Guide. San Francisco, CA: No Starch Press. A highly readable guide to practicing statistics responsibly. Clear and enjoyable.Google Scholar
Smith, RJ. 2018. The continuing misuse of null hypothesis statistical testing in biological anthropology. American Journal of Physical Anthropology 166: 236245. https://doi.org/10.1002/ajpa.23399. Explains how null hypothesis statistical tests are misinterpreted in biological anthropology and why we should use effect sizes and CIs.CrossRefGoogle ScholarPubMed
Vigen, T. 2015. Spurious Correlations. New York: Hachette Books. Humorous examples of spurious correlations in public datasets. Also see www.tylervigen.com/spurious-correlations.Google Scholar
Wasserstein, RL, Lazar, NA. 2016. The ASA’s statement on p-values: context, process, and purpose. The American Statistician 70: 129133. https://doi.org/10.1080/00031305.2016.1154108.CrossRefGoogle Scholar

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