In guarding against inferential mistakes, experimental comparative cognition errs on the side of underattributing sophisticated cognition to animals, or what I refer to as the underattribution bias. I propose eliminating this bias by altering the method of choosing the default, or null, hypothesis. Rather than choosing the most parsimonious null hypothesis, as is current practice, I argue for choosing the best-evidenced hypothesis. Doing so at once preserves the risk-controlling structure of the current statistical paradigm and introduces a sensitivity to probability-conferring empirical and theoretical information. This analysis illustrates how values like parsimony can covertly shape statistical-experimental design and inference.
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