Resolving the Raven Paradox: Simple Random Sampling, Stratified Random Sampling, and Inference to Best Explanation

Abstract

Abstract Simple random-sampling resolutions of the raven paradox relevantly diverge from scientific practice. We develop a stratified random-sampling model, yielding a better fit and apparently rehabilitating simple random sampling as a legitimate idealization. However, neither accommodates a second concern, the objection from potential bias. We develop a third model that crucially invokes causal considerations, yielding a novel resolution that handles both concerns. This approach resembles Inference to the Best Explanation (IBE) and relates the generalization’s confirmation to confirmation of an associated law. We give it an objective Bayesian formalization and discuss the compatibility of Bayesianism and IBE.

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