Statistics interview questions focused on the practical reasoning data roles actually require, not just formulas.
Explain p-value in plain language.
The p-value is the probability of seeing a result at least as extreme as what you observed, assuming there's actually no real effect. A small p-value suggests the observed effect is unlikely to be due to chance alone — though it doesn't tell you the effect is large or important.
What's the difference between correlation and causation?
Correlation means two variables move together; causation means one actually causes the other. Ice cream sales and drowning rates correlate — both rise in summer — but ice cream doesn't cause drowning. Confusing the two leads to bad decisions.
What is a confidence interval?
A range of values that's likely to contain the true population parameter, given the sample data, at a stated confidence level (like 95%). It communicates the uncertainty around an estimate, not just a single point value.
When would you use a t-test versus a chi-square test?
A t-test compares means of numeric data between groups. A chi-square test compares distributions of categorical data. The choice depends on whether you're comparing averages of a continuous variable or the frequency of categories.
What is the Central Limit Theorem and why does it matter?
It states that the distribution of sample means approaches a normal distribution as sample size grows, regardless of the underlying population's distribution. It's why many statistical tests that assume normality still work reasonably well in practice.
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These questions are just the surface
Our Statistics for Data Science program builds the hands-on experience behind these answers — not just memorized responses.
