Analyse experiment results

Team,

Before we write off the shorter checklist as a failure, let’s reframe what the data actually tells us.

“Not statistically significant” does not mean “no impact”—it means our test was underpowered to detect an effect smaller than 3pp. Here is the full picture:

1. The directional signals are uniformly positive: Activation increased by +1.7pp (31.2% → 32.9%). While the 95% CI (−1.4pp to +4.8pp) crosses zero, it leans heavily positive. The downside risk is small, while the potential upside is meaningful.
2. Clear reduction in user friction: Support tickets tagged “onboarding confusion” dropped by 34% (44 to 29). Even if activation were completely flat, cutting nearly half the steps improved user experience and reduced support burden.
3. The cost/benefit is strongly asymmetric: Shipping this takes one day of engineering.

Recommendation: Ship the 5-step checklist to 100% and move on.

Running the test for another four weeks to hit an MDE of 2pp isn’t the best use of our time or traffic. We would burn a month of runway just to confirm what looks like a positive, low-risk change with virtually zero engineering cost.

Let’s bank the operational win (fewer support tickets, leaner UX, likely small activation lift) by rolling it out tomorrow. We can monitor activation metrics in production and direct our experimentation bandwidth toward bigger, higher-leverage bets for Trellis.

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.