If the goal of statistical analysis is to form justified credences based on data, then an account
of the foundations of statistics should explain what makes credences justified. I present a
new account called statistical reliabilism (SR), on which credences resulting from a statistical
analysis are justified (relative to alternatives) when they are in a sense closest, on average, to
the corresponding objective probabilities. This places (SR) in the same vein as recent work on
the reliabilist ju…
Read moreIf the goal of statistical analysis is to form justified credences based on data, then an account
of the foundations of statistics should explain what makes credences justified. I present a
new account called statistical reliabilism (SR), on which credences resulting from a statistical
analysis are justified (relative to alternatives) when they are in a sense closest, on average, to
the corresponding objective probabilities. This places (SR) in the same vein as recent work on
the reliabilist justification of credences generally [Dunn, 2015, Tang, 2016, Pettigrew, 2018],
but it has the advantage of being action-guiding in that knowledge of objective probabilities
is not required to identify the best-justified available credences. The price is that justification
is relativized to a specific class of candidate objective probabilities, and to a particular choice
of reliability measure. On the other hand, I show that (SR) has welcome implications for
frequentist-Bayesian reconciliation, including a clarification of the use of priors; complemen-
tarity between probabilist and fallibilist [Gelman and Shalizi, 2013, Mayo, 2018] approaches
towards statistical foundations; and the justification of credences outside of formal statistical
settings. Regarding the latter, I demonstrate how the insights of statistics may be used to
amend other reliabilist accounts so as to render them action-guiding. I close by discussing
new possible research directions for epistemologists and statisticians (and other applied users
of probability) raised by the (SR) framework.