75 Must-Know Statistics Interview Questions and Answers 2024

Basic Statistical Concepts

1.
What is the difference between descriptive and inferential statistics?
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2.
Define and distinguish between population and sample in statistics.
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3.
Explain what a “distribution” is in statistics, and give examples of common distributions.
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4.
What is the Central Limit Theorem and why is it important in statistics?
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5.
Describe what a p-value is and what it signifies about the statistical significance of a result.
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6.
What does the term “statistical power” refer to?
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7.
Explain the concepts of Type I and Type II errors in hypothesis testing.
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8.
What is the significance level in a hypothesis test and how is it chosen?
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9.
Define confidence interval and its importance in statistics.
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10.
What is a null hypothesis and an alternative hypothesis?
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Probability Theory and Probability Distributions

11.
What is Bayes’ Theorem, and how is it used in statistics?
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12.
Describe the difference between discrete and continuous probability distributions.
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13.
Explain the properties of a Normal distribution.
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14.
What is the Law of Large Numbers, and how does it relate to statistics?
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15.
What is the role of the Binomial distribution in statistics?
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16.
Explain the difference between joint, marginal, and conditional probability.
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17.
How does the Poisson distribution differ from the Normal distribution?
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18.
What is a cumulative distribution function (CDF)?
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19.
Describe the use cases of the Exponential distribution and Uniform distribution.
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20.
How is Covariance different from Correlation?
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Descriptive Statistics and Data Summarization

21.
What are measures of central tendency, and why are they important?
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22.
Explain measures of dispersion: Range, Interquartile Range (IQR), Variance, and Standard Deviation.
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23.
What is the difference between mean and median, and when would you use each?
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24.
How would you describe skewness and kurtosis in a dataset?
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25.
What is the five-number summary in descriptive statistics?
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Statistical Inference and Hypothesis Testing

26.
Explain the steps in conducting a hypothesis test.
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27.
Describe how a t-test is performed and when it is appropriate to use.
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28.
What is ANOVA (analysis of variance), and when is it used?
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29.
Explain the concepts of effect size and Cohen’s d.
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30.
How do you perform a Chi-squared test, and what does it tell you?
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31.
What is a nonparametric statistical test, and why might you use one?
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Regression and Correlation Analysis

32.
What is linear regression, and when is it used?
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33.
How do you interpret R-squared and adjusted R-squared in the context of a regression model?
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34.
Explain the assumptions underlying linear regression.
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35.
What is multicollinearity, and why is it a problem in regression analyses?
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36.
Explain the difference between correlation and causation.
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37.
How can you detect and remedy heteroscedasticity in a regression model?
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38.
What is logistic regression, and how does it differ from linear regression?
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Time Series Analysis

39.
What is a time series, and what makes it different from other types of data?
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40.
Explain autocorrelation and partial autocorrelation in the context of time series.
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41.
What is stationarity in a time series, and why is it important?
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42.
Describe some methods to make a non-stationary time series stationary.
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43.
What is ARIMA, and how is it used for forecasting time series data?
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Dimensionality Reduction and Factor Analysis

44.
What is the purpose of dimensionality reduction in data analysis?
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45.
Explain Principal Component Analysis (PCA) and its applications.
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46.
How does Factor Analysis differ from PCA?
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47.
What is the curse of dimensionality?
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48.
What is Singular Value Decomposition (SVD), and how is it used in Machine Learning?
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Experiment Design and A/B Testing

49.
What is A/B testing, and why is it an important tool in statistics?
Answer:
50.
How do you design an A/B test and determine the sample size required?
Answer:
51.
What are control and treatment groups in the context of an experiment?
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52.
Explain how you would use hypothesis testing to analyze the results of an A/B test.
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53.
How can you avoid biases when conducting experiments and A/B tests?
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Bayesian Statistics

54.
What defines Bayesian statistics, and how does it differ from frequentist statistics?
Answer:
55.
Explain what a prior, likelihood, and posterior are in Bayesian inference.
Answer:
56.
Describe a scenario where applying Bayesian statistics would be advantageous.
Answer:
57.
What is Markov Chain Monte Carlo (MCMC), and where is it used in statistics?
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58.
How would you update a Bayesian model with new data?
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Coding Challenges in Statistics

59.
Write Python code to calculate mean, median, and mode from a given list of numbers.
Answer:
60.
Generate and visualize 1,000 random points from a Normal distribution in Python.
Answer:
61.
Implement a simple linear regression model from scratch in Python.
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62.
Simulate the Monty Hall problem in Python and analyze the results.
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63.
Create a Python function to perform a t-test given two sample datasets.
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64.
Write a Python script to compute and graphically display a correlation matrix for a given dataset.
Answer:
65.
Implement the Metropolis-Hastings algorithm for a simple Bayesian inference simulation.
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66.
Create a Python program that estimates Pi using a Monte Carlo simulation.
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67.
Write a Python code snippet for performing a Chi-squared test of independence on a contingency table.
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68.
Develop a Python function to convert a non-stationary time series into a stationary one.
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69.
Write an R script to conduct an ANOVA test on a given dataset.
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70.
Implement PCA for dimensionality reduction on a high-dimensional dataset in Python.
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Case Studies and Scenario-Based Questions

71.
How would you assess which factors contribute most to sales in a supermarket chain?
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72.
Describe your approach to determining whether a new drug is effective based on clinical trial data.
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73.
Explain how you would evaluate the success of an online advertising campaign with statistical analysis.
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74.
Discuss how you would use time series analysis to forecast stock prices.
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75.
How would you design a statistical study to understand customer churn in a subscription-based business?
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75 Statistics interview questions

What is the difference between descriptive and inferential statistics?

Define and distinguish between population and sample in statistics.

Explain what a “distribution” is in statistics, and give examples of common distributions.

What is the Central Limit Theorem and why is it important in statistics?

Describe what a p-value is and what it signifies about the statistical significance of a result.

What does the term “statistical power” refer to?

Explain the concepts of Type I and Type II errors in hypothesis testing.

What is the significance level in a hypothesis test and how is it chosen?

Define confidence interval and its importance in statistics.

What is a null hypothesis and an alternative hypothesis?

What is Bayes’ Theorem, and how is it used in statistics?

Describe the difference between discrete and continuous probability distributions.

Explain the properties of a Normal distribution.

What is the Law of Large Numbers, and how does it relate to statistics?

What is the role of the Binomial distribution in statistics?

Explain the difference between joint, marginal, and conditional probability.

How does the Poisson distribution differ from the Normal distribution?

What is a cumulative distribution function (CDF)?

Describe the use cases of the Exponential distribution and Uniform distribution.

How is Covariance different from Correlation?

What are measures of central tendency, and why are they important?

Explain measures of dispersion: Range, Interquartile Range (IQR), Variance, and Standard Deviation.

What is the difference between mean and median, and when would you use each?

How would you describe skewness and kurtosis in a dataset?

What is the five-number summary in descriptive statistics?

Explain the steps in conducting a hypothesis test.

Describe how a t-test is performed and when it is appropriate to use.

What is ANOVA (analysis of variance), and when is it used?

Explain the concepts of effect size and Cohen’s d.

How do you perform a Chi-squared test, and what does it tell you?

What is a nonparametric statistical test, and why might you use one?

What is linear regression, and when is it used?

How do you interpret R-squared and adjusted R-squared in the context of a regression model?

Explain the assumptions underlying linear regression.

What is multicollinearity, and why is it a problem in regression analyses?

Explain the difference between correlation and causation.

How can you detect and remedy heteroscedasticity in a regression model?

What is logistic regression, and how does it differ from linear regression?

What is a time series, and what makes it different from other types of data?

Explain autocorrelation and partial autocorrelation in the context of time series.

What is stationarity in a time series, and why is it important?

Describe some methods to make a non-stationary time series stationary.

What is ARIMA, and how is it used for forecasting time series data?

What is the purpose of dimensionality reduction in data analysis?

Explain Principal Component Analysis (PCA) and its applications.

How does Factor Analysis differ from PCA?

What is the curse of dimensionality?

What is Singular Value Decomposition (SVD), and how is it used in Machine Learning?

What is A/B testing, and why is it an important tool in statistics?

How do you design an A/B test and determine the sample size required?

What are control and treatment groups in the context of an experiment?

Explain how you would use hypothesis testing to analyze the results of an A/B test.

How can you avoid biases when conducting experiments and A/B tests?

What defines Bayesian statistics, and how does it differ from frequentist statistics?

Explain what a prior, likelihood, and posterior are in Bayesian inference.

Describe a scenario where applying Bayesian statistics would be advantageous.

What is Markov Chain Monte Carlo (MCMC), and where is it used in statistics?

How would you update a Bayesian model with new data?

Write Python code to calculate mean, median, and mode from a given list of numbers.

Generate and visualize 1,000 random points from a Normal distribution in Python.

Implement a simple linear regression model from scratch in Python.

Simulate the Monty Hall problem in Python and analyze the results.

Create a Python function to perform a t-test given two sample datasets.

Write a Python script to compute and graphically display a correlation matrix for a given dataset.

Implement the Metropolis-Hastings algorithm for a simple Bayesian inference simulation.

Create a Python program that estimates Pi using a Monte Carlo simulation.

Write a Python code snippet for performing a Chi-squared test of independence on a contingency table.

Develop a Python function to convert a non-stationary time series into a stationary one.

Write an R script to conduct an ANOVA test on a given dataset.

Implement PCA for dimensionality reduction on a high-dimensional dataset in Python.

How would you assess which factors contribute most to sales in a supermarket chain?

Describe your approach to determining whether a new drug is effective based on clinical trial data.

Explain how you would evaluate the success of an online advertising campaign with statistical analysis.

Discuss how you would use time series analysis to forecast stock prices.

How would you design a statistical study to understand customer churn in a subscription-based business?

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