Why are my experiment results not showing statistical significance?

There are a few common reasons experiment results fail to show statistical significance:

1. The effect is too small
   The intervention may truly help, but only a little. If the difference between groups is small relative to the noise in the data, the p-value may stay above your threshold.

2. Sample size is too low
   With too few observations, your test may not have enough power to detect a real effect. Even a meaningful improvement can look “non-significant” if the experiment is underpowered.

3. High variability in the data
   If outcomes vary a lot across users, sessions, or days, the noise can drown out the signal.

4. The experiment isn’t well targeted
   The treatment may only affect a subset of users, but you’re analyzing everyone together. That can dilute the effect.

5. The metric is not sensitive enough
   Some metrics respond slowly or are too coarse to capture the impact of your change.

6. Implementation issues
   Bugs, tracking errors, sample-ratio mismatch, or inconsistent exposure can weaken or obscure the effect.

7. Wrong analysis assumptions
   The statistical test may not match the data structure, or you may have ignored clustering, repeated measures, or unequal variances.

8. Multiple comparisons / peeking
   If you check results many times or test many metrics, apparent significance can be harder to interpret and true signals can be mixed with noise.

What to check next:

• Verify randomization and tracking
• Look at sample size and statistical power
• Inspect variance and distribution of the metric
• Segment the data to see if effects are concentrated
• Confirm the analysis method matches the experiment design
• Estimate the minimum detectable effect for your sample size

If you want, I can help you diagnose it using your experiment setup, metric, sample size, and observed lift.