Course note — Reading a result without fooling yourself

Revised Reading a result without fooling yourself this week. The change was small and the reason was not.

The experiment finished. Here is the order to look at things in, and the specific ways a real-looking result turns out not to be.

The first draft explained the mechanism and stopped. What was missing was the failure: the thing that happens when you get it wrong, described concretely enough that someone recognises it later. A lesson that only teaches the correct version leaves you unable to spot the incorrect one, which is the situation you will actually be in.

It sits in the Experiments and causal claims module, and the exercise runs against ab-test-checkout. That pairing is deliberate: the dataset was built with the trap the lesson describes already in it, so the exercise fails in the instructive way rather than the confusing one.

Ordering matters here more than in most courses. It follows Designing an experiment that can succeed and leads into Causal claims when you cannot randomise, and reading it out of sequence mostly works but costs you the setup.

Free means free, and it also means we can rewrite it whenever it is wrong. No edition, no errata PDF, nothing to repurchase. The whole course is 36 lessons and the fixes land the day we find them.