✅ Data Science Interview Questions with Answers Part-2

11. What is the difference between mean, median, and mode?

The mean is the average value calculated by dividing the sum of all values by the total count. The median is the middle value when data is sorted. The mode is the most frequently occurring value. Mean is sensitive to extreme values, while median handles outliers better. Mode is useful for categorical or repetitive data.

12. What is standard deviation and variance?

Variance measures how far data points spread from the mean by averaging squared deviations. Standard deviation is the square root of variance and is expressed in the same unit as the data. A high standard deviation shows high variability, while a low value shows data clustered around the mean.

13. What is probability distribution?

A probability distribution describes how likely different outcomes are for a random variable. It shows the relationship between values and their probabilities. Common examples include normal, binomial, and Poisson distributions. Distributions help model uncertainty and make statistical inferences.

14. What is normal distribution and where is it used?

Normal distribution is a symmetric, bell-shaped distribution where mean, median, and mode are equal. Most values lie near the center and fewer at the extremes. It is widely used in statistics, hypothesis testing, quality control, and natural phenomena such as heights, errors, and measurement noise.

15. What is skewness and kurtosis?

Skewness measures the asymmetry of a distribution. Positive skew has a long right tail, negative skew has a long left tail. Kurtosis measures how heavy the tails are compared to a normal distribution. High kurtosis indicates more extreme values, while low kurtosis indicates flatter distributions.

16. What is correlation vs causation?

Correlation measures the strength and direction of a relationship between two variables. Causation means one variable directly affects another. Correlation does not imply causation because two variables may move together due to coincidence or a third factor. Decisions based only on correlation can be misleading.

17. What is hypothesis testing?

Hypothesis testing is a statistical method used to make decisions using data. It starts with a null hypothesis that assumes no effect or difference. Data is analyzed to determine whether there is enough evidence to reject the null hypothesis in favor of an alternative hypothesis.

18. What are Type I and Type II errors?

A Type I error occurs when a true null hypothesis is rejected, also called a false positive. A Type II error occurs when a false null hypothesis is not rejected, also called a false negative. Reducing one often increases the other, so balance depends on business risk.

19. What is p-value?

A p-value measures the probability of observing results as extreme as the sample data assuming the null hypothesis is true. A small p-value indicates strong evidence against the null hypothesis. It helps decide whether results are statistically significant.

20. What is confidence interval?

A confidence interval provides a range of values within which the true population parameter is expected to lie with a certain level of confidence. For example, a 95 percent confidence interval means the method captures the true value in 95 out of 100 similar samples.

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