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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, summarized by a p-value. The trap is reading the p-value as the probability the null is true, ignoring effect size, or running many tests and reporting only the winners. Applied-AI 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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