Interview Q&A / Data Science

1. What are the main sampling methods: simple random, stratified, cluster and systematic?

   Simple random gives everyone an equal chance; stratified splits the population into groups and samples each; cluster picks whole groups at random; systematic takes every kth item. Each trades cost against precision.

   BeginnerQuick answer1 min read
2. What is sampling bias, and what are common types?

   Sampling bias happens when the sample systematically differs from the population, so conclusions do not generalise. Common types are selection bias, survivorship bias, non-response bias and convenience sampling.

   BeginnerQuick answer1 min read
3. What is the Central Limit Theorem, and why does it matter?

   The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as the sample size grows, whatever the shape of the original data (given finite variance). That is why normal-based tests and confidence intervals work on messy real data.

   BeginnerQuick answer1 min read
4. What is the difference between standard deviation and standard error?

   Standard deviation describes how spread out individual data points are. Standard error describes how much a sample statistic, usually the mean, would vary from sample to sample, and equals SD divided by the square root of n.

   BeginnerQuick answer1 min read
5. How does sample size affect the margin of error in a survey?

   For a proportion, margin of error is about 1.96 x sqrt(p(1-p)/n). It shrinks with the square root of n, so 1000 respondents give roughly +/- 3% and 400 give roughly +/- 5%, and reducing it further gets expensive quickly.

   IntermediateQuick answer1 min read