StreamCart launched a new in-app onboarding tooltip meant to increase adoption of its saved-payment feature. The analytics team wants a quick statistical analysis and a simple visualization plan in Python or R to determine whether the tooltip changed adoption meaningfully.
You are given results from a randomized experiment comparing the old experience (control) with the new tooltip (treatment). Determine whether the treatment increased the saved-payment adoption rate, quantify the uncertainty, and describe what visualizations you would produce to communicate the result.
| Group | Users Exposed | Users Who Adopted Saved Payment | Adoption Rate |
|---|---|---|---|
| Control | 8,400 | 1,176 | 14.0% |
| Treatment | 8,100 | 1,296 | 16.0% |
Additional parameters:
| Parameter | Value |
|---|---|
| Significance level | 0.05 |
| Test type | Two-proportion z-test |
| Confidence level | 95% |