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

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.

This is a preview of subscription content, log in to check access.

Notes

  1. 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. 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. 3.

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

  4. 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. 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. 6.

    We thank Elliott Sober for raising this point.

  7. 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. 8.

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

  9. 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.

References

  1. Bausman WC (2018) Modeling: neutral, null, and baseline. Philos Sci 84(4):414–435

    Google Scholar 

  2. Beatty J (1987) Natural selection and the null hypothesis. In: Dupre J (ed) The latest on the best. MIT Press, Cambridge

    Google Scholar 

  3. Beatty J (1997) Why do biologists argue like they do?”. Philos Sci 64((Proceedings)):S432–S443

    Article  Google Scholar 

  4. Bell G (2000) The distribution of abundance in neutral communities. Am Nat 155(5):606–617

    Article  Google Scholar 

  5. Call J, Tomasello M (2008) Does the chimpanzee have a theory of mind? 30 years later. Trends Cognit Sci 12(5):187–192

    Article  Google Scholar 

  6. Chase JM, Leibold MA (2003) Ecological niches: linking classical and contemporary approaches. University of Chicago Press, Chicago

    Google Scholar 

  7. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Routledge Academic, New York

    Google Scholar 

  8. Dacey M (2016) The varieties of parsimony in psychology. Mind Lang 31(4):414–437

    Article  Google Scholar 

  9. Dienes Z (2008) Understanding psychology as a science: an introduction to scientific and statistical inference. Palgrave Macmillan, New York

    Google Scholar 

  10. Douglas H (2009) Science, policy, and the value-free ideal. University of Pittsburgh Press, Pittsburgh

    Google Scholar 

  11. Fitzpatrick S (2008) Doing away with Morgan’s Canon. Mind Lang 23(2):224–246

    Article  Google Scholar 

  12. Fitzpatrick S (2017) Against Morgan’s Canon. In: Andrews K, Beck J (eds) The Routledge handbook of philosophy of animal minds. Taylor and Francis, Routledge

  13. Fletcher L, Carruthers P (2013) Behavior-reading versus mentalizing in animals. In: Metcalfe J, Terrace HS (eds) Agency and joint attention. Oxford University Press, Oxford, pp 82–99

    Google Scholar 

  14. Forster M, Sober E (1994) How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. Br J Philos Sci 45(1):1–35

    Article  Google Scholar 

  15. Gigerenzer G (2004) Mindless statistics. J Socio-Econo 33(5):587–606

    Article  Google Scholar 

  16. Godfrey-Smith P (1994) Of nulls and norms. In: PSA: proceedings of the Biennial meeting of the Philosophy of Science Association

  17. Gotelli NJ, Graves GR (1996) Null models in ecology. Smithsonian Institution Press, Washington DC

    Google Scholar 

  18. Halina M (2015) There is no special problem of mindreading in nonhuman animals. Philos Sci 82(3):473–490

    Article  Google Scholar 

  19. Hare B, Call J, Tomasello M (2006) Chimpanzees deceive a human competitor by hiding. Cognition 101(3):495–514

    Article  Google Scholar 

  20. Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography. In: Levin SA, Horn HS (eds) Monographs in population biology. Princeton University Press, Princeton

    Google Scholar 

  21. Hubbell SP (2006) Neutral theory and the evolution of ecological equivalence. Ecology 87(6):1387–1398

    Article  Google Scholar 

  22. Krupenye C, Kano F, Hirata S, Call J, Tomasello M (2016) Great apes anticipate that other individuals will act according to false beliefs. Science 354(6308):110–114

    Article  Google Scholar 

  23. Levins R (1966) The strategy of model building in population biology. Amer Sci 54:421–431

    Google Scholar 

  24. Lloyd EA (2015) Adaptationism and the logic of research questions: how to think clearly about evolutionary causes. Biol Theory 10:1–20

    Article  Google Scholar 

  25. Longino HE (2008) Values, heuristics, and the politics of knowledge. In: Carrier M, Howard D, Kourany J (eds) The challenge of the social and the pressure of practice: science and values revisited. University of Pittsburgh Press, Pittsburgh, pp 68–86

    Google Scholar 

  26. Lurz RW (2011) Mindreading animals: the debate over what animals know about other minds. MIT Press, Cambridge

    Google Scholar 

  27. MacArthur RH (1957) On the relative abundance of bird species. Proc Natl Acad Sci USA 43(3):293

    Article  Google Scholar 

  28. Mayo DG, Spanos A (2006) Severe testing as a basic concept in a Neyman–Pearson philosophy of induction. Br J Philos Sci 57(2):323–357

    Article  Google Scholar 

  29. Meketa I (2014) A critique of the principle of cognitive simplicity in comparative cognition. Biol Philos 29(5):731–745

    Article  Google Scholar 

  30. Melis AP, Call J, Tomasello M (2006) Chimpanzees (Pan troglodytes) conceal visual and auditory information from others. J Comp Psychol 120(2):154

    Article  Google Scholar 

  31. Millstein RL (2017) Genetic drift. In: Zalta EN (ed) The Stanford encyclopedia of philosophy, Fall 2017 edn. https://plato.stanford.edu/archives/fall2017/entries/genetic-drift/

  32. Neyman J, Pearson ES (1933) The testing of statistical hypotheses in relation to probabilities a priori. In: Mathematical proceedings of the Cambridge Philosophical Society

  33. Norton JD (2003) A material theory of induction. Philos Sci 70(4):647–670

    Article  Google Scholar 

  34. Penn DC, Povinelli DJ (2007) On the lack of evidence that non-human animals possess anything remotely resembling a ‘theory of mind’. Philos Trans R Soc Lond B: Biol Sci 362(1480):731–744

    Article  Google Scholar 

  35. Penn DC, Povinelli DJ (2009) On becoming approximately rational: the relational reinterpretation hypothesis. In: Watanabe S, Blaisdell AP, Huber L, Young A (eds) Rational animals, irrational humans. Keio University Press, Tokyo, pp 23–43

    Google Scholar 

  36. Penn DC, Povinelli DJ (2013) The comparative delusion: the “behavioristic/mentalistic” dichotomy in comparative theory of mind research. In: Metcalfe J, Terrace HS (eds) Agency and joint attention. Oxford University Press, Oxford, pp 62–82

    Google Scholar 

  37. Penn DC, Holyoak KJ, Povinelli DJ (2008) Darwin’s mistake: explaining the discontinuity between human and nonhuman minds. Behav Brain Sci 31(02):109–130

    Google Scholar 

  38. Povinelli DJ, Vonk J (2004) We don’t need a microscope to explore the chimpanzee’s mind. Mind Lang 19(1):1–28

    Article  Google Scholar 

  39. Purves DW, Pacala SW (2005) Ecological drift in niche-structured communities: neutral pattern does not imply neutral process. In: Burslem D, Pinard M, Hartley S (eds) Biotic interactions in the tropics: their role in the maintenance of species diversity. Cambridge University Press, Cambridge, pp 107–138

    Google Scholar 

  40. Rosenbaum PR (2005) Observational study. In: Everitt BS, Howell DC (eds) Encyclopedia of statistics in behavioral science. Wiley, Chichester

    Google Scholar 

  41. Sani F, John T (2008) Experimental design and statistics for psychology: a first course. Wiley, Hoboken

    Google Scholar 

  42. Sedlmeier P, Gigerenzer G (1989) Do studies of statistical power have an effect on the power of studies? Psychol Bull 105(2):309

    Article  Google Scholar 

  43. Sober E (1994) Let’s razor ockham’s razor. In: Sober E (ed) From a biological point of view: essays in evolutionary philosophy. Cambridge University Press, Cambridge, pp 136–157

    Google Scholar 

  44. Sober E (2002) Instrumentalism, parsimony, and the Akaike framework. Philos Sci 69(S3):S112–S123

    Article  Google Scholar 

  45. Sober E (2005) Comparative psychology meets evolutionary biology: Morgan’s Canon and Cladistic Parsimony. In: Daston L, Mitman G (eds) Thinking with animals: new perspectives on anthropomorphism. Columbia University Press, New York, pp 85–99

    Google Scholar 

  46. Sober E (2009) Parsimony and models of animal minds. In: Lurz RW (ed) The philosophy of animal minds, p 237. Cambridge University Press, Cambridge

    Google Scholar 

  47. Staley KW (2017) Pragmatic warrant for frequentist statistical practice: the case of high energy physics. Synthese 194(2):355–376

    Article  Google Scholar 

  48. Tilman D (1982) Resource competition and community structure. In: Levin SA, Horn HS (eds) Monographs in population biology. Princeton University Press, Princeton

    Google Scholar 

  49. Tilman D (1986) Resources, competition and the dynamics of plant communities. In: Crawley M (ed) Plant ecology. Blackwell Scientific Publications, Oxford, pp 51–75

    Google Scholar 

  50. Vellend M (2016) The theory of ecological communities. In: Levin SA, Horn HS (eds) Monographs in population biology. Princeton University Press, Princeton

    Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to William Bausman.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Null hypothesis
  • Community ecology
  • Neutral theory
  • Comparative psychology
  • Mindreading hypohesis
  • Reasoning strategy