Contingency Tables on Adventures in Why

Contingency Tables Part IV: The Score Test

bob@ConvexAnalytics.com (Bob Wilson) — Tue, 24 Nov 2020 00:00:00 +0000

The score test can be used to calculate p-values and confidence intervals for A/B tests. The score test considers the slope of the likelihood function at the parameter value associated with the null hypothesis.

Sprinkle some Maximum Likelihood Estimation on that Contingency Table!

bob@ConvexAnalytics.com (Bob Wilson) — Sun, 08 Nov 2020 00:00:00 +0000

Maximum Likelihood Estimation provides consistent estimators, and can be efficiently computed under many null hypotheses of practical interest.

Contingency Tables Part II: The Binomial Distribution

bob@ConvexAnalytics.com (Bob Wilson) — Sun, 01 Nov 2020 00:00:00 +0000

In our last post, we introduced the potential outcomes framework as the foundational framework for causal inference. In the potential outcomes framework, each unit (e.g. each person) is represented by a pair of outcomes, corresponding to the result of the experience provided to them (treatment or control, A or B, etc.)

Contingency Tables Part I: The Potential Outcomes Framework

bob@ConvexAnalytics.com (Bob Wilson) — Sat, 24 Oct 2020 00:00:00 +0000

“Why can’t I take the results of an A/B test at face value? Who are you, the statistics mafia? I don’t need a PhD in statistics to know that one number is greater than another.” If this sounds familiar, it is helpful to remember that we do an A/B test to learn about different potential outcomes. Comparing potential outcomes is essential for smart decision making, and this framework is the cornerstone of causal inference.

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.