01
Explain p-value in simple words.
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The probability of seeing a result at least this extreme if the null hypothesis were true. A small p-value (for example < 0.05) is evidence against the null hypothesis.
Statistics Interview Questions for Data Science
5 Statistics questions with answers for data science roles. Tick what you have solved to track your progress.
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02
What is the Central Limit Theorem?
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The distribution of sample means approaches a normal distribution as the sample size grows (roughly 30+), whatever the population's shape. It lets us build confidence intervals and run hypothesis tests on means.
03
Type I vs Type II error?
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Type I = false positive (rejecting a true null), probability α. Type II = false negative (missing a real effect), probability β. Power = 1 − β.
04
How do you design an A/B test?
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Define the hypothesis and one primary metric, calculate sample size from the baseline, minimum detectable effect, α and power, randomise users, run for full business cycles, check for sample-ratio mismatch, then test both statistical and practical significance.
05
Correlation vs covariance?
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Covariance shows the direction of a relationship but depends on units. Correlation is covariance scaled to −1…+1, so it shows direction and strength and is unit-free.