Abstract
In this article I give a critical evaluation of the use and limitations of null-model-based hypothesis testing as a research strategy in the biological sciences. According to this strategy, the null model based on a randomization procedure provides an appropriate null hypothesis stating that the existence of a pattern is the result of random processes or can be expected by chance alone, and proponents of other hypotheses should first try to reject this null hypothesis in order to demonstrate their own hypotheses. Using as an example the controversy over the use of null hypotheses and null models in species co-occurrence studies, I argue that null-model-based hypothesis testing fails to work as a proper analog to traditional statistical null-hypothesis testing as used in well-controlled experimental research, and that the random process hypothesis should not be privileged as a null hypothesis. Instead, the possible use of the null model resides in its role of providing a way to challenge scientists’ commonsense judgments about how a seemingly unusual pattern could have come to be. Despite this possible use, null-model-based hypothesis testing still carries certain limitations, and it should not be regarded as an obligation for biologists who are interested in explaining patterns in nature to first conduct such a test before pursuing their own hypotheses.
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Notes
In species co-occurrence studies, when claiming that a species exists, occurs, or is present on an island, ecologists typically mean that the species has established a breeding population on that island instead of just having several vagile individuals.
For a detailed discussion of the differences between neutral models and null models, see Gotelli and McGill (2006).
In species co-occurrence studies, the null models constructed by different ecologists may be more or less different from each other. Even Connor and Simberloff themselves keep modifying their null models in later publications. Nevertheless, the version I will introduce here, which appears in one of their earliest and also most-cited publications on this subject, helps demonstrate the key features of null-model-based hypothesis testing.
Although the term “randomization test” is often used interchangeably with “permutation test,” actually they are different. A randomization test is based on random assignment involved in experimental design; the procedure of random assignment is conducted before empirical data are collected. By contrast, a permutation test is a nonparametric method of statistical hypothesis testing based on data resampling.
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Acknowledgements
I wish to acknowledge the great help of Michael Weisberg, Erol Akçay, Jay Odenbaugh, and two anonymous reviewers for suggestions on improving the manuscript. An earlier draft of this article was also presented in the Philosophy of Science Reading Group at the University of Pennsylvania, the Salon of Philosophy of Science and Technology at Tsinghua University in Beijing, and PBDB 13 (Philosophy of Biology at Dolphin Beach) in Moruya, Australia. I want to thank the participants of these meetings, who asked valuable questions that inspired this article.
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Zhang, M. The use and limitations of null-model-based hypothesis testing. Biol Philos 35, 31 (2020). https://doi.org/10.1007/s10539-020-09748-0
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DOI: https://doi.org/10.1007/s10539-020-09748-0