A/B Testing and Iterative Improvement
Explore how to design A/B tests that isolate single variables and ensure trustworthy results. Understand how to use specific feedback to correct issues and verify improvements by re-evaluating with consistent datasets for continuous system optimization.
The metrics and test framework from the previous lesson exist to be used, not just to sit in place. Two questions come up constantly once they do: how do you compare two candidate designs against each other and trust the result, and once an evaluation finds a real weakness, how do you actually fix it rather than just noting that something needs to improve? This lesson covers both, and the two turn out to depend on the same underlying discipline: isolating exactly what changed and being specific about exactly what was wrong. In this lesson, we will cover:
Designing an A/B test that isolates the one variable being tested, rather than comparing two designs that differ in several ways at once
How much data a comparison needs before a measured difference can be trusted over ordinary output variance
Why generic feedback fails to change what a system ...