Felt okay about the pandas part but I second-guessed myself on why Welch specifically versus a standard t-test.
Start by clarifying the data schema and assumptions (e.g., table names, column names, date format, and whether 'July 2024' means the full month). Then outline a pandas-based solution: filter rides to San Francisco and July 2024, join with experiment assignments on ride_id or user_id, split into treatment and control groups, and run scipy.stats.ttest_ind with equal_var=False to get the p-value. Finally, mention edge cases like missing assignments or multiple rides per user.
Pro tip: Always validate the join and group sizes before testing—if the treatment and control groups are imbalanced or have missing data, the p-value may be misleading. Also, consider whether the metric is per-ride or per-user, as this affects the independence assumption of the t-test.
Ask about table schemas, column names, date handling, and whether the analysis is at ride or user level. Confirm the definition of 'time-to-pickup' and the experiment assignment logic.
Use pandas to filter rides to San Francisco and July 2024, then join with experiment assignments on the appropriate key (e.g., ride_id or user_id). Handle missing assignments appropriately.
Separate the joined data into treatment and control groups. Check sample sizes, missing values, and outliers. Consider whether the t-test assumptions (independence, normality) are reasonable.
Use scipy.stats.ttest_ind with equal_var=False on the time-to-pickup values for treatment and control. Extract and return the p-value.
Report the p-value along with group means and confidence intervals. Discuss practical significance and any caveats (e.g., multiple testing, novelty effects).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying that statistical significance depends on both the p-value and the pre-defined significance level (alpha), typically 0.05. Then explain that you compare the p-value to alpha, but also consider practical significance, effect size, and the context of the experiment. Emphasize that a p-value alone is not sufficient; you must also check assumptions and avoid common pitfalls like multiple testing.
Pro tip: Mention that at a company like Waymo, where safety is critical, you would also look at confidence intervals and effect sizes to assess practical significance, not just statistical significance. This shows you understand the business context and go beyond textbook definitions.
Compare the p-value to the pre-determined significance level (alpha). If p-value < alpha, reject the null hypothesis and declare statistical significance.
Ensure the test's assumptions (e.g., independence, normality, random sampling) are met, and that the experiment was properly designed and executed.
Evaluate the effect size and confidence interval to determine if the result is meaningful in the real-world context, not just statistically significant.
If multiple comparisons were made, apply corrections (e.g., Bonferroni, FDR) to control the family-wise error rate or false discovery rate.
Combine statistical evidence with business impact, costs, and risks to make a final recommendation or decision.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by directly stating that a statistically significant p-value is not conclusive proof, as it only indicates that the observed effect is unlikely under the null hypothesis. Then, systematically discuss the main threats to validity, including practical significance, multiple testing, novelty effects, and external factors. Conclude by emphasizing the need for a holistic evaluation combining statistical, practical, and business considerations.
Pro tip: Mention that at a company like Waymo, where safety and reliability are paramount, even a statistically significant improvement must be weighed against operational constraints and potential risks. Highlight the importance of replication and out-of-sample validation to ensure robustness.
Explain that a p-value measures the probability of observing the data (or more extreme) assuming the null hypothesis is true. It does not measure the probability that the null hypothesis is true or that the alternative is true.
Discuss that a small p-value does not imply a large or meaningful effect. The effect size and confidence interval should be considered to assess practical significance.
List and explain threats such as multiple comparisons (p-hacking), novelty effects, selection bias, confounding variables, and external validity issues (e.g., sim-to-real gap).
Emphasize the importance of proper randomization, sufficient sample size, and controlling for variables. In the context of routing algorithms, consider metrics like safety, latency, and user experience.
Suggest combining statistical evidence with domain knowledge, business impact, and replication studies. Advocate for a decision-making framework that weighs risks and benefits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current experiment design and its limitations, then propose a stronger design that addresses those gaps, such as using a switchback or cluster-randomized design to handle interference. Outline a rigorous analysis plan that includes pre-registration, power analysis, and appropriate statistical methods to estimate causal effects.
Pro tip: Emphasize the importance of pre-registering the analysis plan and simulating the experiment to validate design assumptions, as this demonstrates rigor and foresight that senior data scientists at Waymo value.
Ask questions to understand the existing routing algorithm, the current experiment design, and the key metrics (e.g., safety, efficiency, passenger comfort). Identify the specific limitations or concerns with the current approach.
Suggest a design that mitigates interference and bias, such as a switchback design (alternating algorithms over time) or cluster randomization (randomizing at the level of geographic regions or fleets). Discuss trade-offs and justify your choice.
Specify primary and secondary metrics, including safety-critical ones. Conduct a power analysis to determine sample size and experiment duration, considering variance and minimum detectable effect.
Detail the statistical methods for analysis, such as intent-to-treat, difference-in-differences, or Bayesian hierarchical models. Include plans for handling missing data, outliers, and multiple comparisons.
Discuss how to ensure internal and external validity, including randomization checks, blinding, and monitoring for novelty effects. Consider operational constraints like safety regulations and real-world deployment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.