  Players are statistical learners who learn about payoffs from data. They may
interpret the same data differently, but have common knowledge of a class of
learning procedures. I propose a metric for the analyst's "confidence" in a
strategic prediction, based on the probability that the prediction is
consistent with the realized data. The main results characterize the analyst's
confidence in a given prediction as the quantity of data grows large, and
provide bounds for small datasets. The approach generates new predictions, e.g.
that speculative trade is more likely given high-dimensional data, and that
coordination is less likely given noisy data.
