Randomization Inference on Adventures in Why

Randomization Inference for Matched Pairs with Binary Outcomes

bob@ConvexAnalytics.com (Bob Wilson) — Thu, 03 Sep 2026 00:00:00 +0000

Modes of Inference in Randomized Experiments

bob@ConvexAnalytics.com (Bob Wilson) — Sat, 13 Jan 2024 00:00:00 +0000

Randomization provides the “reasoned basis for inference” in an experiment. Yet some approaches to analyzing experiments ignore the special structure of randomization. Simple, familiar approaches like regression models sometimes give wrong answers when applied to experiments. Approaches exploiting randomization deliver more reliable inferences than methods neglecting it. Randomization inference should be the first method we reach for when analyzing experiments.

Attributable Effects

bob@ConvexAnalytics.com (Bob Wilson) — Sat, 29 Jul 2023 00:00:00 +0000

In a previous post, we discussed why randomization provides a reasoned basis for inference in an experiment. Randomization not only quantifies the plausibility of a causal effect but also allows us to infer something about the size of that effect.

The Reasoned Basis for Inference in Experiments

bob@ConvexAnalytics.com (Bob Wilson) — Sun, 04 Jun 2023 00:00:00 +0000

In his 1935 book, “Design of Experiments”, Ronald Fisher described randomization as the “reasoned basis for inference” in an experiment. Why do we need a “basis” at all, let alone a reasoned one?

Fisher's Exact Test

bob@ConvexAnalytics.com (Bob Wilson) — Sun, 25 Mar 2018 00:00:00 +0000

Simulation-based inference sits on a rigorous foundation.