ab-testing
3 free lessons tagged ab-testing across Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
When your A/B test is lying anyway
You randomized correctly and read the statistics honestly, and the experiment can still give the wrong answer, because the clean logic assumes things that are not always true. Learn how users affecting each other breaks the method, why a short test misjudges a long-run effect, how an average hides opposite effects in subgroups, and the reflex of distrusting results that look too good.
The traps that make A/B tests lie
A perfectly randomized experiment can still hand you a confident, completely false result. Learn why, from the coin-flip nature of chance to the statistical significance that tells you less than you think. Covers p-values and what they really mean, why peeking at a running test wrecks it, how testing many things guarantees false wins, and why a result must be big enough to matter, not just real.
Why before-and-after fools you
You change the button, sales rise, you conclude the button worked, and you may be completely wrong. Learn why comparing before and after is one of the most reliable ways to fool yourself, what a controlled experiment does differently, why randomization is the whole trick, and how the A/B test turns a guess about cause into something you can actually measure.