← Walmart Labs Interview Insights
I knew the general answer but fumbled the vocabulary under pressure.
Start by clarifying the test parameters: baseline conversion rate, minimum detectable effect (MDE), significance level (alpha), and power (1-beta). Then explain the formula or simulation approach to compute sample size per variant, and discuss practical considerations like traffic allocation and test duration.
Pro tip: Always discuss the trade-offs between MDE, sample size, and test duration—smaller MDEs require exponentially more samples, which may not be feasible. Also, mention that for Walmart Labs, you'd often use sequential testing or Bayesian methods to allow early stopping without inflating Type I error.
Identify the primary metric (e.g., conversion rate) and its current baseline value from historical data.
Determine the significance level (alpha, typically 0.05), power (1-beta, typically 0.8), and the minimum detectable effect (MDE) you care about.
Use the standard formula for proportions or means, or simulation for complex metrics. For proportions: n = (Z_{1-alpha/2} + Z_{1-beta})^2 * (p1(1-p1) + p2(1-p2)) / (p2-p1)^2.
Plug in the values to get the required sample size per variant. Adjust for expected traffic and any known variances.
Check if the required sample is achievable given traffic and time. Consider sequential testing or Bayesian methods if not.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This follow-up was where it got interesting.
Start by clarifying that statistical power depends on effect size, variance, sample size, and significance level. Then systematically discuss how to increase power by reducing variance, increasing effect size, or adjusting design parameters—without simply adding more data. Emphasize practical trade-offs and Walmart-scale considerations.
Pro tip: Mention that at Walmart Labs, you often can't just collect more data due to traffic constraints or business urgency, so techniques like CUPED or stratification are essential. Also, highlight that increasing power sometimes means accepting a higher false positive rate (alpha) if the cost of a false negative is high.
Explain that power = f(effect size, variance, sample size, alpha). Since sample size is fixed, focus on the other three levers.
Discuss techniques like stratification, blocking, using covariates (CUPED), paired designs, and removing outliers or using robust statistics.
Suggest focusing on a more sensitive metric, using a stronger treatment, or targeting a subpopulation with a larger expected effect.
Consider increasing alpha (if appropriate), using one-sided tests, or sequential testing with alpha spending to maintain power.
Mention techniques like variance reduction via machine learning, Bayesian methods, or using historical data to inform priors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.