Analyse experiment results

Subject: Shorter onboarding checklist: what the test tells us and what to do

Bottom line: This test didn't fail. It was inconclusive, and I recommend we ship the shorter checklist.

What the result says - Activation was 32.9% vs. 31.2% (+1.7pp). The 95% CI runs from −1.4 to +4.8pp, so "not significant" means we can't rule out zero. It doesn't mean there's no effect. - The data are compatible with a real gain of several points, and also with a small loss. The test was powered to detect 3pp, so a true effect of 1-2pp would probably look just like this. - Support tickets tagged "onboarding confusion" dropped from 44 to 29. The counts are small, so this isn't proof, but it points the same way as the activation estimate.

Why ship rather than keep testing - Shipping costs about a day of engineering, and it's easy to reverse. - The plausible downside is small (about −1.4pp at worst), and the point estimate and the ticket data both lean positive. - Four more weeks would detect ~2pp, but our observed effect is 1.7pp. We could easily wait a month and end up inconclusive again, while half of new users get the longer flow.

Next steps 1. Ship the five-step checklist to everyone. 2. Track seven-day activation against the pre-test baseline for the next 4-6 weeks. If it drops well below ~31%, we revert. 3. Keep tracking the onboarding-confusion tickets. 4. Record the test as "inconclusive, shipped on cost and risk grounds" rather than "failed," so nobody later cites it as evidence that shorter onboarding doesn't work.

If we were weighing a costly or hard-to-reverse change, I'd argue for the longer test. Here the cheaper move is to ship and monitor.

Team—

This test is inconclusive, not a failure. Activation was 32.9% with the shorter checklist versus 31.2% with the original—a +1.7 percentage-point estimate. But the 95% confidence interval (−1.4 to +4.8 points) leaves room for a modest decline, no effect, or a useful improvement. We haven’t established that the shorter checklist helps—or that it doesn’t.

The test was designed to detect a 3-point effect, so it had limited ability to resolve a smaller gain. That 3-point minimum detectable effect is a planning parameter, not a threshold below which improvements are worthless.

Confusion-tagged support tickets fell from 44 to 29. That’s encouraging, but without comparable exposure counts and uncertainty estimates, it’s supporting evidence rather than proof.

My recommendation: proceed with a staged rollout, retaining a randomized holdout. The shorter flow is already built and takes about a day to ship, making a monitored rollout reasonable—not risk-free. Track seven-day activation and confusion tickets per onboarded user, and agree on rollback criteria before expanding.

Another four weeks would improve sensitivity to roughly a 2-point effect, but would not guarantee a definitive answer. Continue the full test instead if distinguishing a gain of that size would materially change our shipping decision.

Let’s decide based on likely value, downside risk, and implementation cost—not whether the result crossed a significance threshold.

Team,

This test is inconclusive, not a failure. Activation increased from 31.2% to 32.9%, but the confidence interval spans a 1.4 percentage-point decline to a 4.8-point improvement. We haven’t established a benefit or ruled out meaningful harm.

The pre-registered 3pp minimum detectable effect describes what we designed the test to detect reliably. It isn’t a threshold below which improvements have no value. A smaller lift could still be worthwhile.

Confusion tickets fell from 44 to 29, which is encouraging supporting evidence. Without comparable rates by experiment arm, consistent tagging and exposure counts, we can’t attribute that reduction to the shorter checklist.

My recommendation is a staged rollout, provided basic checks reveal no downstream harm. With only a day of engineering needed, I wouldn’t automatically spend another four weeks pursuing statistical significance. The remaining decision is whether the likely benefit justifies the remaining cost and risk.

Before rollout, confirm every included user has had the full seven-day activation window and check experiment allocation, tracking and any available downstream engagement measures. Compare confusion-ticket rates between arms.

Then increase exposure gradually, retaining a randomised holdout and agreeing monitoring dates and a rollback threshold upfront. This is a practical decision under uncertainty, not a declaration that B won.

Extend the experiment instead if a possible 1.4pp activation loss is commercially unacceptable¹ or if establishing a roughly 2pp improvement would change our decision. If we extend, pre-specify the analysis and stopping rule; four more weeks offers greater precision, not a guaranteed answer.