This is the kind of question that sounds manageable until you're staring at messy logs and realize half your events are duplicated or missing timestamps.
Start by outlining a structured data cleaning process: handle missing values, remove duplicates, and validate that the data is properly randomized. Then compute conversion rates per group, calculate lift and a two-sample t-test or chi-square test for p-value, and create a clear visualization like a bar chart with confidence intervals. Finally, interpret the results in business terms, considering practical significance and potential next steps.
Pro tip: Always check for sample ratio mismatch (SRM) before analyzing results—if the groups aren't split as expected, the experiment may be invalid. Also, remember that statistical significance doesn't always mean business significance; discuss effect size and confidence intervals.
Load the raw log data into a pandas DataFrame, handle missing values, remove duplicates, and ensure each user is assigned to only one group. Check for sample ratio mismatch (SRM) to validate randomization.
Calculate conversion rate for control and treatment groups, compute the absolute and relative lift, and perform a statistical test (e.g., two-proportion z-test or chi-square) to obtain the p-value.
Create a bar chart with error bars (confidence intervals) for conversion rates by group, and optionally a histogram or boxplot of key metrics to show distribution differences.
Explain the statistical significance (p-value) and practical significance (lift, confidence interval) in business terms. Discuss potential impact on key metrics like bookings or revenue, and recommend next steps (e.g., roll out, iterate, or gather more data).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.