Paul Ellis provides a very accessible introduction to several basic topics that are vital to good research practice. In order to encourage readers who enjoyed this book to dive deeper into the literature, I feel the need to point out that the content is slightly out of date on several counts; two points that immediately come to mind are (1) the non-critical presentation (or even the outright recommendation?) of the flawed “fail-safe N” method (see Becker, 2005; Ioannidis, 2008; Sutton, 2009; Ferguson & Heene, 2012 for criticisms, or this Cochrane handbook link for a brief summary) and (2) the lack of mention of viable methods to provide support for null-hypotheses (e.g., equivalence testing or Bayesian methods).

That should, however, not take away from the fact that the text offers an easily comprehensible treatment of the subject matter, and I would recommend it to anyone who happens to be searching for a non-technical introduction to the topics that it covers.

Becker, B. J. (2005). Failsafe N or file-drawer number. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis. Sussex: Wiley.
Ferguson, C. J., and Heene, M. (2012). A vast graveyard of undead theories: Publication bias and psychological science’s aversion to the null. Perspectives on Psychological Science, 7, 555–561. doi: 10.1177/1745691612459059
Ioannidis, J. P. A. (2008). Interpretation of tests of heterogeneity and bias in meta-analysis. Journal of Evaluation in Clinical Practice, 14, 951–957. doi: 10.1111/j.1365-2753.2008.00986.x
Sutton, A. J. (2009). Publication bias. In H. Cooper, L. Hedges, and J. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 435–452). New York: Russell Sage Foundation.

The essential guide to effect sizes (Paul Ellis)

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