Inferential Stats

Inferential Statistics Interview Q&A

Concepts used to generalize from samples to populations.

1What is inferential statistics?easy
Answer: It uses sample data to make conclusions about a larger population.
2What is a population vs sample?easy
Answer: Population is full set; sample is a subset used for analysis.
3What is sampling bias?medium
Answer: It occurs when sample is not representative, leading to misleading inference.
4What is confidence interval?medium
Answer: A range of plausible parameter values with an associated confidence level (e.g., 95%).
5What is null hypothesis?easy
Answer: Baseline claim (H0), often representing no effect or no difference.
6What is alternative hypothesis?easy
Answer: Competing claim (H1/Ha) that there is an effect or difference.
7What is p-value?medium
Answer: Probability of seeing data at least as extreme as observed if H0 were true.
8What is significance level (alpha)?easy
Answer: Threshold for rejecting H0, commonly 0.05.
9Type I and Type II errors?medium
Answer: Type I: reject true H0 (false positive). Type II: fail to reject false H0 (false negative).
10What is test power?medium
Answer: Probability of correctly rejecting a false null hypothesis (1−beta).
11When to use t-test?medium
Answer: Compare means when sample size is small and/or population variance unknown.
12When to use chi-square test?medium
Answer: For categorical data: goodness-of-fit or independence between categories.
13What is ANOVA?medium
Answer: It tests whether means of 3 or more groups differ significantly.
14Why statistical significance is not practical significance?hard
Answer: A tiny effect can be statistically significant with large samples but still have low business impact.
15One-line inferential stats summary?easy
Answer: Inferential statistics turns sample evidence into defensible decisions under uncertainty.
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