On the Foundations of Statistical Inference, I: Binary Experiments (Classic Reprint)

Explore how binary experiments shape modern statistical inference and decision making.

This book examines how outcomes from simple binary tests inform conclusions about hypotheses. It connects classic ideas from Neyman–Pearson to contemporary notions of evidence and decision procedures, all within a rigorous probabilistic framework.

The discussion stays concrete, showing how different inference methods relate to error probabilities, likelihoods, and practical interpretation. Readers will see how the same mathematical model can support varying conclusions across different contexts, while still maintaining objective probabilistic justification.

• How binary experiments are formalized and compared within a unified algebra of statistics
• How error probabilities and likelihood ratios influence conclusions
• The role of the likelihood function in informative inference
• How multi‑decision and intrinsic justification extend classical testing methods

Ideal for readers of foundational statistics and decision theory seeking a rigorous, model‑based view of how evidence guides conclusions.