If you are looking for Data Science Statistics Interview Questions then this article is all you need for your statistics subject preparation. Statistics is an important part of the Data science syllabus where you must be familiar with the formulas and implementation of the logics in the calculation.
In this blog, let us collect some of the most frequently asked data science statistics interview questions for 2025 for everyone including freshers, working professionals and intermediate candidates.
Is Statistics a Part of the Data Science Syllabus?
Yes, Statistics is an important part of the latest data science curriculum. Data science combines mathematics, computer science, statistics, and other domain expertise to help candidates master extracting useful insights from the raw data.
Statistics is an important concept in data science which serves as the backbone of data science as it provides important tools and frameworks methods to extract important insights from the data available to data scientists. These data science statistics interview questions will help you prepare more effectively for your interview and get familiar with what type of questions are more frequently asked during the interview rounds.
Top 17 Data Science Statistics Interview Questions For Freshers With Answers
Let us now gather some of the best data science statistics interview questions below along with the answers.
Q1: Explain the Central Limit Theorem and give examples of real-world applications.
The Central Limit Theorem (CLT) states that, regardless of the original distribution of a dataset, the sampling distribution of the sample mean will be approximately normal if the sample size is sufficiently large (usually n>30n>30). This is crucial in inferential statistics, as it allows us to make predictions about populations using sample data.
Some popular examples of the Central Limit Theorem preferred in data science statistics interview questions are mentioned below.
- Quality Control: Manufacturers can use CLT to analyze product defects by taking small sample batches instead of testing every product.
- Finance & Stock Market: Analysts use CLT to estimate expected returns from historical stock prices.
Q2: Briefly explain A/B testing and its applications.
A/B testing is a statistical method used to compare two versions of a product, webpage, or marketing strategy to determine which performs better. Users are randomly assigned to either Group A (control) or Group B (treatment), and statistical tests determine if the difference is significant.
Some major applications used in the A/B testing in data science are mentioned below.
- Website Optimization: Testing different layouts to improve user engagement.
- Marketing Campaigns: Evaluating which email subject line gets more clicks.
- Pharmaceuticals: Testing drug effectiveness compared to a placebo.
Q3: What are Hypothesis Testing & P-Value in Layman’s Terms?
Hypothesis Testing is like a courtroom trial: we assume a defendant is innocent (null hypothesis) and only reject that assumption if there’s strong evidence (p-value).
- Null Hypothesis (H0H0): There’s no real effect (e.g., “This drug has no impact”).
- Alternative Hypothesis (H1H1): There is an effect (e.g., “This drug improves health”).
- P-Value: The probability of observing the data if H0H0 were true. A small p-value (e.g., <0.05) suggests rejecting H0H0.
Q4: Derive the conclusions from a Left-Skewed Distribution with Median = 60
A left-skewed distribution means the tail is longer on the left side. In such a case:
- Mean < Median < Mode.
- The mean is pulled to the left (lower) due to outliers.
- The mode (most frequent value) is higher than the median.
Q5: What is Selection Bias and How to Avoid It?
Selection bias occurs when the sample is not representative of the population, leading to misleading conclusions.
There are many ways to avoid a selection bias in the data science statistics methods.
- Random Sampling: Ensures all individuals have an equal chance of selection.
- Stratified Sampling: Divides the population into subgroups before sampling.
- Double-Blinding: In experiments, neither subjects nor researchers know group assignments.
Q6: What are Long-Tailed Distribution and their Importance in data science and statistics?
A long-tailed distribution has a higher probability of extreme values. They are important because of their importance in classification where they can handle imbalance datasets and regression where they prevent models from being dominated by extreme values in the dataset.
Let us consider some of the examples to understand.
- Wealth Distribution: Few people hold extreme wealth.
- Social Media Popularity: A few posts go viral, while most get few views.
- Online Retail Sales: Some products sell in huge quantities while most have low demand.
Q7: What is KPI in Statistics?
A Key Performance Indicator (KPI) is a measurable value that indicates how well a business or process is achieving its goals. Let us take few examples to understand it better.
- Sales Growth (% increase in sales)
- Customer Retention Rate (% of returning customers)
- Conversion Rate (% of website visitors making purchases)
Q8: What is a coin Flip Experiment (Hypothesis & P-Value)?
- Null Hypothesis (H0H0): The coin is fair (probability of heads = 0.5).
- Alternative Hypothesis (H1H1): The coin is biased.
- P-Value Calculation: Using a binomial test, we check the probability of getting ≤1 head in 10 flips under H0H0.
Q9: What are major considerations for Multiple Hypothesis Testing?
- Bonferroni Correction: Adjusts significance levels to control false positives.
- False Discovery Rate (FDR): Controls the expected proportion of incorrect rejections.
- Power Analysis: Ensures sufficient sample size for valid conclusions.
Q10: What are the preliminary conditions for the Central Limit Theorem?
- Independence: Samples must be independent.
- Random Sampling: Data should be drawn randomly.
- Sample Size: Typically n>30n>30 for normal approximation.
- Finite Variance: Population variance should exist.
Q11: What is Skewness & How to Measure It?
Skewness measures asymmetry in a distribution. It can be measured using the pearson’s skewness and Moment based skewness methods.
Q12: How can we estimate dd from Uniform Distribution
Best estimate can be calculated using the given expression: d≈(n+1)n×max(X)d≈n(n+1)×max(X) (from maximum likelihood estimation).
Q13: Give a simple overview of Chi-Square, ANOVA, and t-Test
- Chi-Square Test: Tests categorical independence.
- ANOVA: Compares means of 3+ groups.
- t-Test: Compares means of 2 groups.
Q14: What is Blending Mean & Std Dev Across Subsets?
For K subsets with means μk and variances σ^2k:
Blended Mean:
Blended Variance:
Q15: Difference between the Significance Level vs Confidence Level.
- Significance Level (αα): Probability of rejecting H0H0 when it’s true.
- Confidence Level (1−α1−α): Probability the true parameter lies in the interval.
Q16: What does the Law of Large Numbers (LLN) signify?
LLN states that as a sample size grows, its average converges to the population mean. It is used in data science to improve the predictions along with more data in the dataset.
Q17: Difference between Confidence Interval vs Prediction Interval
- Confidence Interval measures the range for a population mean.
- Prediction Interval measures the range for future individual data points.
Top 10 Data Science Statistics Interview Questions For Working Professionals
Let us pick some of the handpicked data science statistics interview questions for experienced professionals who are looking to switch in other organisation for a data science role.
1. What is the p-value, and how do you interpret it?
The p-value represents the probability of obtaining results as extreme as the observed data, assuming H0H0 is true.
- p < 0.05: Strong evidence to reject H0H0 (significant result).
- p > 0.05: Not enough evidence to reject H0H0.
For example: In A/B testing, if p-value = 0.02, it means there is a 2% probability that the observed difference is due to chance.
2. What is the difference between correlation and causation?
- Correlation: Measures the strength and direction of the relationship between two variables but does not imply one causes the other.
- Causation: A change in one variable directly influences another.
3. What is the difference between a confidence interval and a prediction interval?
- Confidence Interval (CI): Estimates the range within which the population parameter (mean) is expected to lie.
- Prediction Interval (PI): Estimates the range within which a future observation is expected to fall.
4. What is the Law of Large Numbers, and how is it used in data science?
The Law of Large Numbers (LLN) states that as a sample size increases, the sample mean approaches the population mean.
- In machine learning, LLN ensures that larger training datasets provide more reliable models.
- In A/B testing, large samples reduce variability in test results.
5. Explain the concept of Maximum Likelihood Estimation (MLE).
MLE or Maximum Likelihood Estimation is a method for estimating parameters of a statistical model by maximizing the likelihood function.
For example, In a normal distribution, MLE estimates mean (μ) and variance (σ^2) by finding values that maximize the probability of observed data. It is important to provide a real world example in your data science statistics interview questions.
6. What is heteroscedasticity, and why is it a problem in regression?
Heteroscedasticity occurs when the variance of errors in a regression model is not constant, violating one of the assumptions of Ordinary Least Squares (OLS) regression.
7. What is bootstrapping in statistics?
Bootstrapping is a resampling technique that creates multiple simulated samples from the observed dataset with replacement to estimate confidence intervals or model parameters.
8. What are the assumptions of linear regression?
- Linearity – Relationship between independent and dependent variables is linear.
- Independence – Observations are independent (no autocorrelation).
- Homoscedasticity – Constant variance of residuals.
- Normality – Residuals follow a normal distribution.
- No multicollinearity – Independent variables should not be highly correlated.
9. How do you handle missing data in a dataset?
- Remove missing data: If missingness is small and random.
- Mean/Median Imputation: Replace with column mean/median.
- KNN Imputation: Fill in missing values using nearest neighbors.
- Predictive Modeling: Use regression models to estimate missing values.
- Multiple Imputation: Generate multiple plausible datasets for better accuracy.
10. What is heteroscedasticity, and why is it a problem in regression?
Heteroscedasticity occurs when the variance of errors in a regression model is not constant, violating one of the assumptions of Ordinary Least Squares (OLS) regression. Some common issues with heteroscedasticity are unreliable standard errors and it also affects hypothesis testing (inflated Type I errors). These type of questions are very much important for your data science statistics interview questions.
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Data Science Statistics Interview Questions FAQs
Q1. What is the future of data science in 2025?
Ans: The future of data science is going to be very bright in 2025 as more and more uses of data are increasing which requires more needs for extracting useful insights from the data.
Q2. Will AI replace data science?
Ans: AI is not going to replace data scientists however the operation of data science might see many changes with automation of tasks. However, data scientists will require more profound domain expertise, critical thinking, problem solving and more.
Q3. Is statistics included in the data science syllabus?
Ans: Yes, data science syllabus consists of statistics as one of its important subjects along with mathematics, algorithms, computer science and more.
Q4. Who can refer to the data science statistics interview questions?
Ans: These data science statistics interview questions are suitable for beginners, intermediate as well as experienced professionals.
