Equation 38 · Evolutionary Biology and Ecology in Practice: An Advanced Technical Guide
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Across all three workflows, the recurring lesson is the same: the statistical machinery — capture-history models, LD-based estimators, maximum-entropy distribution surfaces — is only as trustworthy as the design decisions made before any data are collected and the validation decisions made after a model is fit. A goodness-of-fit test that is skipped, a linked-locus pair left in an calculation, or a distribution model validated against its own training region rather than an independent one will each produce a number that looks exactly like a real estimate and is not one. None of the peer-reviewed methods surveyed here promise to remove that judgment; each one exists specifically to…
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Across all three workflows, the recurring lesson is the same: the statistical machinery — capture-history models, LD-based estimators, maximum-entropy distribution surfaces — is only as trustworthy as the design decisions made before any data are collected and the validation decisions made after a model is fit. A goodness-of-fit test that is skipped, a linked-locus pair left in an calculation, or a distribution model validated against its own training region rather than an independent one will each produce a number that looks exactly like a real estimate and is not one. None of the peer-reviewed methods surveyed here promise to remove that judgment; each one exists specifically to make the judgment checkable, by supplying a diagnostic — a goodness-of-fit statistic, a sample-size correction term, a spatially independent validation score — that turns “trust the estimate” into “here is the test the estimate had to pass.”
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