Me too! Design Predictor helps you turn early data into real confidence, fast. It uses Bayesian magic (aka stats that look into the future) to help you spot winning designs before running a full A/B test. Think of it as your shortcut to smarter & faster design decisions, so you can test more, gain confidence, and launch with certainty.
| Variant | Estimated Conversion | CI Lower | CI Upper |
|---|---|---|---|
| D1 | -- | -- | -- |
| D2 | -- | -- | -- |
| Interpretation | D1 | D2 |
|---|---|---|
| Is one design better than the other? | -- | -- |
| Can I trust my data? | -- | |
| Behavior shift detected? | -- | |
| Recommendation | -- | |
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P-Value (iykyk...)
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|---|---|
| Probability of getting this result if there’s actually no difference. | -- |
| Statistically Significant? | -- |
| Effect Size (h) | Confidence (↑) | Recommendation |
|---|---|---|
| Very High (≥ 0.8) | ≥ 95% | 🚀 Launch - big win! |
| Very High (≥ 0.8) | 80–94% | ✅ Strong result — worthy of rolling out. |
| High (0.5–0.79) | ≥ 95% | 🏁 Launch — meaningful improvement |
| High (0.5–0.79) | 80–94% | ✅ Likely a win — prioritize for rollout |
| High (0.5–0.79) | < 80% | 🤔 Promising — validate further |
| Medium (0.2–0.49) | ≥ 95% | 📈 Real change — launch if aligned with goals |
| Medium (0.2–0.49) | 80–94% | 🔁 Mild signal — consider launch |
| Medium (0.2–0.49) | < 80% | 💤 Too uncertain — needs more data |
| Low (< 0.2) | ≥ 95% | 🪄 Launch — subtle change good at scale |
| Low (< 0.2) | 80–94% | 📉 Likely noise — monitor or deprioritize |
| Low (< 0.2) | < 80% | 💤 No signal |