statistics
AI, ML & GenAI 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, a sample-ratio mismatch signals a bug, and network effects and novelty break naive tests. For ML, it is how you confirm an offline improvement really helps online, since offline gains often do not hold. AI, ML, and GenAI engineer interviews probe it because shipping on offline metrics alone is a classic mistake.📊 Evaluation & ML Foundations
Core
MLE, MAP, and Bayesian vs FrequentistMaximum likelihood chooses the parameters that make the observed data most probable; MAP adds a prior and chooses the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior serves as regularization. AI, ML, and GenAI engineer interviews probe this to check whether you know 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 a sample mean is approximately normal no matter the underlying distribution, which is why so much inference relies on the normal curve. Standard error captures how much a sample mean wobbles and shrinks as sample size grows, unlike standard deviation. AI, ML, and GenAI engineer interviews probe this because it fixes 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, 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.📊 Evaluation & ML FoundationsSign in