Data & Machine Learning
Statistics & Probability
9 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- A stakeholder points to a chart showing users who use feature X have much higher retention and concludes that feature X causes retention. What's wrong with that reasoning, and how would you push back constructively?
- When you're testing many metrics or hypotheses at once, why does that inflate your false-positive rate, and how do you correct for it?Go Pro
- An A/B test you ran shows the new model variant is better with p = 0.03. A product stakeholder wants to ship it. How do you interpret that result for them, and what else do you look at before recommending the rollout?Go Pro
- What does a p-value actually mean, and what are the most common ways people misinterpret statistical significance?Go Pro
- Can you explain the Central Limit Theorem and why it's useful in practice?Go Pro
- A colleague reports a model's F1 went from 0.84 to 0.86 and wants to call it an improvement. How would you put error bars on a metric like F1 to judge whether that gap is real, and when does that push you to a bootstrap rather than a closed-form interval?Go Pro
- Your A/B test comes back with a p-value of 0.03. What does that 0.03 actually mean, and what's a common way people misread it?Go Pro
- How would you explain a confidence interval to a non-technical stakeholder, and when would you use bootstrapping to compute one?Go Pro
- How do you decide when a problem needs ML versus a simpler rule-based or statistical approach?Go Pro
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