Novelty Effect in A/B Testing: How to Detect It
What the novelty effect is, how to tell a real decaying effect from a statistical artifact, and the three checks that separate a fad from a durable win.
Complete, honest guides on CRO, A/B testing and experimentation. The most complete articles on the web, no fluff. (page 5 of 17)
What the novelty effect is, how to tell a real decaying effect from a statistical artifact, and the three checks that separate a fad from a durable win.
What sample ratio mismatch is, how the chi-square check works, how small an imbalance already invalidates a test, and how to find the root cause.
GrowthBook vs PostHog compared by criterion: statistics engine, self-hosting weight, SDK role, data source, and which team profile each one actually fits.
How many visitors an A/B test needs, with a full reference table by baseline rate and effect size, the arithmetic behind it, and what to do on low traffic.
What minimum detectable effect means, how to pick an MDE you can defend, absolute versus relative, and what your traffic can actually resolve.
A/B vs multivariate testing in practice: a comparison table, the interaction effect, the real sample cost per cell and a decision tree with a calculator.
What AI copilots in A/B testing tools really do, what they cannot do by construction, and a script to evaluate one without outsourcing the decision.
Personalization vs A/B testing: the different questions each one answers, the real traffic cost of segmenting and a decision rule with a calculator.
AI-generated A/B test variations: what the evidence shows, the statistical cost of testing many at once and how to use them without breaking rigor.