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Not null enough: pseudo-null hypotheses in community ecology and comparative psychology

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Abstract

We evaluate a common reasoning strategy used in community ecology and comparative psychology for selecting between competing hypotheses. This strategy labels one hypothesis as a “null” on the grounds of its simplicity and epistemically privileges it as accepted until rejected. We argue that this strategy is unjustified. The asymmetrical treatment of statistical null hypotheses is justified through the experimental and mathematical contexts in which they are used, but these contexts are missing in the case of the “pseudo-null hypotheses” found in our case studies. Moreover, statistical nulls are often not epistemically privileged in practice over their alternatives because failing to reject the null is usually a negative result about the alternative, experimental hypothesis. Scientists should eschew the appeal to pseudo-nulls. It is a rhetorical strategy that glosses over a commitment to valuing simplicity over other epistemic virtues in the name of good scientific and statistical methodology.

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Notes

  1. The language of “null hypothesis” comes from Fisher's single hypothesis testing procedure, and Neyman and Pearson objected to its use, but it has long been a part of Neyman–Pearson testing. See (Gigerenzer 2004).

  2. John Beatty (1987) discusses the case of Kimura's neutral theory of molecular evolution as a null hypothesis with respect to Fisher's version of null hypothesis testing and explains why it is inappropriate.

  3. For general introductions to statistical hypothesis testing see Sani and Todman (2008), Dienes (2008).

  4. We describe the statistical reasoning here in terms of experiments, but do not intend to exclude observational studies. When experiments are impractical, observational studies are done instead. In an experiment, participants are randomly assigned to the control or experimental group, while in an observational study, participants are selected by uncontrolled factors. Our account of statistical hypothesis testing extends to observational studies insofar as they employ the standard tools of statistical inference used in Neyman–Pearson testing. On the principles of designing an observational study and how to detect, minimize, and measure biases, see Rosenbaum (2005).

  5. This is controversial and depends in part on how selection and drift are conceptualized. See Millstein (2017, Sect. 2) for an overview of the fault lines.

  6. We thank Elliott Sober for raising this point.

  7. Other model selection criteria exist, such as the Bayesian information criteria. While there are important differences between these selection criteria, our arguments here extend to them as well.

  8. Such a discussion between simplicity and other scientific virtues may be locally (Levins 1966) or generally (Douglas 2009) justified.

  9. We have focused on simplicity because it is directly appealed to by the proponents of the behavior-reading and neutrality hypotheses to justify privileging them. But we can imagine another virtue being privileged at the cost of all others, generality for example, and we would be against its use as a way of treating two hypotheses asymmetrically and calling the more general one the ‘null’ just the same.

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Acknowledgements

Versions of this paper were presented at POBAM 2014 and SPSP 2015. We thank the audience for their questions and discussion. We would also like to thank Adrian Currie, Shay Logan, Helen Longino, Elliott Sober, Kent Staley, Jos Uffink, and C. Kenneth Waters for extensive comments and discussion. William Bausman’s writing of this article was supported in part by a grant from the John Templeton Foundation: #50191; From Biological Practice to Scientific Metaphysics.

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Bausman, W., Halina, M. Not null enough: pseudo-null hypotheses in community ecology and comparative psychology. Biol Philos 33, 30 (2018). https://doi.org/10.1007/s10539-018-9640-4

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