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Core
Hypothesis Testing and p-values
Hypothesis testing asks whether an observed effect is large enough to be unlikely under a null hypothesis of no effect, condensed into a p-value. The trap is treating the p-value as the probability the null is true, overlooking effect size, or running many tests and reporting only the winners. AI, ML, and GenAI engineer interviews probe it because it is the inference engine behind A/B testing and any claim that a model change actually helped.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceExplain hypothesis testing: null/alternative, p-value, Type I/II errors, and choosing a test.→Machine Learning & Data ScienceDesign an A/B test for a model change: power, sample size, significance, and the peeking problem.→Machine Learning & Data ScienceExplain MLE vs MAP and apply Bayes' theorem to a medical-test (base-rate) problem.→Machine Learning & Data ScienceHow do you tell whether model A is genuinely better than model B, not just better by chance?→Machine Learning & Data ScienceExplain the Central Limit Theorem, and the difference between correlation and causation (with Simpson's paradox).→Machine Learning & Data ScienceWalk through the common probability distributions and when each applies.→
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