The deduplication step is where I stumbled first.
Start by clarifying the business context and assumptions (e.g., timezone, definition of confirmed booking, cancellation handling). Then outline a step-by-step data pipeline: deduplicate visits, attribute bookings, and compute metrics. Finally, provide SQL or Python code with clear comments and discuss trade-offs.
Pro tip: Mention that you would validate the deduplication logic by checking the distribution of visit intervals and ensuring no legitimate visits are dropped, and consider using window functions for efficiency.
Ask clarifying questions about timezone, definition of confirmed booking, cancellation handling, and whether the 7-day window is rolling or fixed. State assumptions explicitly.
Use a window function to rank visits per user ordered by timestamp descending, and keep only the latest visit within each 60-second window. Ensure that deduplication is done per user and that the window is based on the visit timestamp.
For each confirmed booking, find the most recent visit by the same user within the prior 24 hours. Use a lateral join or window function to match bookings to visits based on user ID and time difference.
Aggregate data by day and variant (control/treatment) for the 7-day window. Calculate visits, unique users, attributed bookings, conversion rate (attributed bookings / unique users), total revenue, revenue per visit, cancellation counts, and unattributed confirmed booking counts.
Write SQL or Python code with clear comments, explaining each step. Discuss potential pitfalls such as timezone conversions, data volume, and performance optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew Wilson CIs were the right call for proportions with small samples, but I had to look up the exact formula mid-session which was a bit embarrassing.
Start by clarifying the metric definition and data granularity, then outline the computation of daily conversion rates and Wilson confidence intervals for each group. Describe the visualization approach, emphasizing clear distinction between control and treatment with error bars, and discuss how to interpret trends and significance over time.
Pro tip: Mention that Wilson intervals are more reliable for proportions, especially with small daily sample sizes, and consider applying multiple comparison corrections or using sequential testing if daily significance is a concern.
Confirm the definition of conversion rate (e.g., bookings per session) and ensure data is aggregated daily for each group. Check for missing days or anomalies.
For each day and group, calculate the conversion rate and the 95% Wilson confidence interval using the number of successes and trials.
Plot the daily conversion rates for control and treatment as lines, with shaded error bands or error bars representing the Wilson intervals. Use distinct colors and a legend.
Highlight any days where intervals do not overlap, indicating potential significant differences. Discuss trends, seasonality, and practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They basically embedded this into the problem statement rather than asking it outright.
Start by acknowledging that edge cases are inevitable in experimentation and must be handled systematically to maintain validity. Then walk through a structured framework: define clear rules for each edge case, apply them consistently, and validate the impact on results. Emphasize the importance of pre-registration and sensitivity analysis to ensure robustness.
Pro tip: Proactively mention that you would document these edge cases and their handling in the experiment design doc, and run a sensitivity analysis to show results are robust to different handling choices. This demonstrates rigor and prevents post-hoc debates.
Specify how users are assigned to variants (e.g., first exposure wins) and how events are attributed (e.g., 24-hour window from first exposure). Document these rules before the experiment starts.
If a user appears in both control and treatment within the window, use a deterministic rule like first exposure or a hash-based assignment to ensure they are consistently assigned to one variant.
Decide whether to count all bookings or only the first. Consider the business context: if the goal is to measure incremental bookings, counting all may be appropriate, but if it's to measure conversion, the first booking might be more relevant.
Apply the pre-defined attribution window strictly. If a booking falls outside, exclude it from the primary analysis but consider a sensitivity analysis with a longer window to check robustness.
Quantify how many users/bookings are affected by each edge case, run sensitivity analyses, and communicate any assumptions or limitations to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.