Tesla·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Tesla data engineer interview with a meaty statistics and experimental design question. One question, lots of sub-parts, felt more like a take-home prompt crammed into a live conversation.

Questions Asked (1)

You have 5 groups of batteries with random assignment, and each battery goes through 3 pass/fail inspections. How would you design a statistical methodology to determine which group has the best overall quality? Walk through how you'd aggregate the inspection results per battery, which statistical test you'd use to compare the 5 groups, how you'd handle multiple comparisons, what effect size and confidence level you'd report, and what caveats apply around sample size and assumption violations.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This one had a lot of layers and I definitely underestimated how long it would take to work through all of them.

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AI HintsAI Generated

Suggested Approach

Start by defining a per-battery quality score that aggregates the three pass/fail inspections, then compare the five groups using a statistical test appropriate for the score distribution (e.g., ANOVA on proportions or Kruskal-Wallis on ordinal scores). Address multiple comparisons with a correction like Tukey HSD or Bonferroni, and report effect sizes (e.g., Cohen's h or eta-squared) with 95% confidence intervals, while discussing sample size and assumption checks.

Pro tip: Emphasize that the choice of aggregation method (e.g., all-pass vs. count of passes) should align with business priorities—whether a single failure is critical or partial credit is acceptable—and that you'd validate assumptions before selecting a test.

1. Define and aggregate per-battery quality

Decide on a scoring scheme: e.g., binary (all three passes = 1, else 0), count of passes (0-3), or weighted sum. Justify based on whether inspections are equally important and if partial success matters.

2. Choose appropriate statistical test

If using binary all-pass, compare proportions across groups with a chi-square test or logistic regression; if using count/ordinal, use Kruskal-Wallis or ANOVA if assumptions hold. Consider mixed-effects models if batteries are nested within groups.

3. Handle multiple comparisons

Since comparing 5 groups involves 10 pairwise comparisons, apply a correction such as Tukey HSD for ANOVA, or Bonferroni/Holm for non-parametric tests to control family-wise error rate.

4. Report effect size and confidence

Compute effect sizes (e.g., Cohen's h for proportions, eta-squared for ANOVA) and 95% confidence intervals for differences. Report p-values adjusted for multiple comparisons.

5. Discuss caveats and assumptions

Address sample size per group (power analysis), independence of inspections, normality/homogeneity of variance, and potential clustering. Suggest sensitivity analyses or non-parametric alternatives if assumptions violated.

Key Points to Mention

Aggregation method: binary all-pass vs. count of passes vs. weighted score, and its alignment with quality definition.
Statistical test selection: chi-square, ANOVA, Kruskal-Wallis, or logistic regression based on data type and assumptions.
Multiple comparison correction: Tukey HSD, Bonferroni, Holm-Bonferroni, or false discovery rate (FDR).
Effect size measures: Cohen's h, eta-squared, odds ratios, with confidence intervals.
Sample size and power: ensure adequate n per group to detect meaningful differences; consider post-hoc power.
Assumption checks: independence, normality, homogeneity of variance; use non-parametric tests or transformations if violated.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.