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Evidentiary inference in evolutionary biology

Review of Elliott Sober’s (2008) Evidence and evolution: the logic behind the science. Cambridge University Press, New York

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Notes

  1. Strict Bayesian conditionalization requires learning o with certainty, which is implausible for many types of scientific observations. Jeffrey conditionalization allows for belief updating with uncertain observation (Jeffrey 1983).

  2. More precisely, AIC estimates the Kullback–Leibler discrepancy between L(M) and the true curve representing the unknown relationship (see Burham and Anderson 2002, Chap. 2).

  3. The resulting notion is also too narrow. Technological, imaginative, or even budgetary limitations could contingently preclude statements from being testable at any time, but this reflects vicissitudes of human priorities and cognition rather than epistemic deficiency of the statements.

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Acknowledgments

Thanks to Mark Colyvan, Jenann Ismael, Adam LaCaze, Katie Steele, and especially Aidan Lyon and Elliott Sober for helpful feedback. Reading group discussions at the University of Sydney and Florida State University were also beneficial. I am grateful to the Australian Commonwealth Environment Research Facilities Research Hub: Applied Environmental Decision Analysis and the Sydney Centre for the Foundations of Science for research support.

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  1. University of Sydney, Sydney, NSW, Australia

    James Justus

  2. Florida State University, Tallahassee, FL, USA

    James Justus

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Justus, J. Evidentiary inference in evolutionary biology. Biol Philos 26, 419–437 (2011). https://doi.org/10.1007/s10539-010-9205-7

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