5 Analytics Mistakes That Kill Growth Experiments (And How to Fix Them)

Mistake 1: Measuring the wrong metric

The experiment measured click-through rate. But the business cares about revenue. These two things don't always move together — an experiment can lift CTR by 20% while producing zero revenue impact if the users who click through are lower intent. The fix: before running any experiment, define your primary metric (the one that actually reflects business value) and your guardrail metrics (the ones you're not allowed to move negatively). CTR, session duration, and page views are diagnostic metrics — they're useful for understanding why something happened, but they shouldn't be the primary success criterion for an experiment.

Mistake 2: Stopping the experiment too early

You see a 15% lift at day 3. You ship it. A week later, the lift disappears — or reverses. This is called peeking, and it's one of the most common ways teams generate false positives. Statistical significance at any given moment doesn't mean the effect is real or stable — it means the current data is unlikely under the null hypothesis, which changes as more data comes in. The fix: calculate your required sample size before the experiment starts (use the free sample size calculator on this site), then don't evaluate results until you've reached it. Set a calendar reminder. Don't look at results daily.

Mistake 3: Running multiple experiments on the same population

You're testing a new onboarding flow and a new pricing page at the same time, both targeting new signups. Half the users are in both experiments simultaneously. Now your results are contaminated — you can't tell whether the onboarding result was influenced by the pricing variant the user also saw. The fix: implement a mutual exclusion layer in your experimentation tool. In Amplitude Experiment, LaunchDarkly, and most mature tools, you can configure experiment groups to not overlap. If you don't have that capability, run experiments sequentially on the same user population.

Mistake 4: Not accounting for novelty effect

New things get clicks. Users engage with a redesigned UI because it's different — not because it's better. Experiments that measure behavior in the first 48 hours frequently show a positive lift that decays as novelty fades. The fix: for experiments on existing users, extend your measurement window past the novelty period (usually 2 weeks minimum) and check whether the effect is stable in week 2 vs. week 1. For new user experiments, novelty is less of a concern since all users are experiencing your product for the first time.

Mistake 5: Treating inconclusive results as failures

An experiment returns p=0.4 and gets filed away as 'didn't work.' But inconclusive doesn't mean the idea is wrong — it might mean the experiment was underpowered (too small a sample or too short a runtime). It might mean the effect exists but is smaller than you hypothesized. It might mean the metric was too noisy to detect a real signal. The fix: when an experiment is inconclusive, run a post-hoc power analysis. If you were underpowered, re-run with a corrected sample size. If you were adequately powered and still saw no effect, then you can more confidently conclude the idea didn't work. Inconclusive results are information — treat them as such.