statistics
Applied AI interview questions tagged statistics, across every topic.
9 questions · 2 unlocked for you
Concepts behind "statistics"
The curriculum that explains the ideas these questions test.
Foundational
A/B TestingAn A/B test randomly splits users between a control and a variant and compares a metric to measure causal impact. The hard part is validity, not setup: peeking inflates false positives, you need enough power, sample-ratio mismatch signals a bug, and network effects and novelty break naive tests. For ML, it is how you confirm an offline improvement actually helps online, because offline gains often do not hold. Applied-AI interviews probe it because shipping on offline metrics alone is a classic mistake.📊 Evaluation & ML Foundations
Core
MLE, MAP, and Bayesian vs FrequentistMaximum likelihood picks the parameters that make the observed data most probable; MAP adds a prior and picks the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior acts as regularization. Applied-AI interviews probe this to see if you understand where priors enter your models, why L2 regularization is a Gaussian prior in disguise, and the practical split between point estimates and full posteriors.📊 Evaluation & ML FoundationsSign in
Core
CLT, Sampling, and Confidence IntervalsThe central limit theorem says the mean of a sample is approximately normal regardless of the underlying distribution, which is why so much inference uses the normal curve. Standard error measures how much a sample mean wobbles and shrinks with sample size, unlike standard deviation. Applied-AI interviews probe this because it sets how wide a confidence interval is and therefore how long an A/B test must run.📊 Evaluation & ML FoundationsSign in
Core
Hypothesis Testing and p-valuesHypothesis 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.📊 Evaluation & ML FoundationsSign in