Interview Q&A / Data Science

1. What are variance and standard deviation, and why do we need them?

   Variance is the average of the squared distances from the mean, and standard deviation is its square root. They measure how spread out the data is. Two datasets can have the same mean but very different spread, and standard deviation is easier to read because it is in the same units as the data.

   BeginnerIn-depth3 min read
2. What is the difference between correlation and causation?

   Correlation means two variables move together. Causation means a change in one actually produces a change in the other. Correlation can come from a hidden third factor, from chance, or from cause running the other way, so proving causation usually needs a controlled experiment such as an A/B test.

   BeginnerIn-depth3 min read
3. What is the difference between mean, median and mode, and when is the median better?

   The mean is the sum divided by the count, the median is the middle value after sorting, and the mode is the most frequent value. Use the median when the data is skewed or has outliers, like salaries, because one very large value pulls the mean up but barely moves the median.

   BeginnerIn-depth4 min read
4. How do you detect outliers using the IQR and z-score methods?

   With the IQR method, a value is an outlier if it lies below Q1 minus 1.5 times the IQR or above Q3 plus 1.5 times the IQR. With the z-score method, a value is flagged if it is more than about 3 standard deviations from the mean. IQR is more robust for skewed data and small samples, because outliers inflate the mean and standard deviation.

   IntermediateIn-depth4 min read
5. What is hypothesis testing, and what does a p-value actually mean?

   Hypothesis testing checks whether data is surprising under a default assumption called the null hypothesis. The p-value is the probability of seeing results at least as extreme as yours if the null hypothesis were true. A small p-value is evidence against the null. It is not the probability that the null is true.

   AdvancedIn-depth5 min read
6. 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
7. What does a 95% confidence interval actually mean?

   It comes from a procedure that, if repeated on many samples, would capture the true parameter in about 95% of them. It does not mean there is a 95% probability that this one interval contains the truth.

   IntermediateQuick answer1 min read