Reading Results
Once your test is running and has been analyzed, the test detail page shows everything you need to make a decision.
Page layout
The test detail page has a header and three tabs:
Header
- Test name, description, and status badge
- Progress bar showing how far through the test period you are
- Action buttons: Run Analysis, Complete, Archive, Duplicate, Delete
Overview tab
This is where you spend most of your time. It contains:
Recommendation card — the headline: SCALE, KILL, or CONTINUE with a confidence score and supporting checklist. See Recommendations for how these are determined.
Cumulative effect chart — a time-series showing the estimated treatment effect over the test period, with confidence intervals that narrow as data accumulates.
DiD results card — the Difference-in-Differences regression output:
- Point estimate (treatment effect)
- Standard error
- 90% and 95% confidence intervals
- Baseline mean (control group average)
- Observation count
Bayesian card — probability-based results:
- P(positive effect) — probability the experiment helped
- P(exceeds target) — probability you hit your MDE target
- P(harm) — probability the experiment hurt
- 90% credible interval
Guardrail metrics — secondary metrics you're monitoring for side effects. See Guardrail Metrics.
Data quality warnings — automated checks for issues like sparse data, zero variance, or group imbalance. See Data Quality.
Locations tab
Lists all treatment and control locations with names and cities.
Data tab
Shows the underlying dataset information and historical data details.
Analysis states
Your test moves through analysis stages:
| State | Meaning |
|---|---|
| Scheduled | Test is set up but hasn't started yet |
| Collecting Data | Test is running, accumulating observations |
| Ready | Enough data to run analysis |
| Analyzed | Analysis complete, results available |
Click Run Analysis at any time after data collection begins to get fresh results. You can re-run as often as you like—each run uses the latest available data.
You don't have to wait for the test period to end. ProofPod's Bayesian approach lets you check results at any point and will tell you if you have enough data for a confident call.