38 Statistics Interview Questions (With Sample Answers)

Indeed Editorial Team

Updated 19 January 2023

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Interviews for the position of data scientists and analysts usually include questions on statistics and probability. These questions can be of diverse types and generally assess candidates on different kinds of statistical concepts. If you are preparing for a statistics interview, it is important to review some frequently asked questions. In this article, we discuss the different types of statistics interview questions, share some sample answers and provide tips on how to solve statistics interview queries.

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Different Types Of Statistics Interview Questions

There is no one way to categorize the different types of statistics interview questions. Usually, they are a part of interviews for roles that require knowledge of data science and data analytics. Here are the different kinds of questions that employers can ask:

  • Basic: These are simple questions that may ask you to define certain statistical concepts or explain how to compute an answer. These could also include questions related to basic counting, variance, or sample spaces.

  • Probability: Most questions in statistics interviews use probability in some way or the other. These are several concepts and models related to probability, including distribution, variance, expectation, etc.

  • Hypothesis testing: Technically, this is also a type of probability question and includes inference, p-values, errors, and other such concepts. Hypothesis testing questions are common for A/B testing.

  • Advanced statistics: These include complex statistics questions that may require the application of more than one statistical formula or model. Modeling, regression, estimation, and other concepts used in machine learning can be a part of such questions.

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Example Statistics Interview Questions And Answers

Here are some statistics interview questions and answers for you to review:

Define descriptive statistics and its characteristics

This question assesses your understanding of descriptive statistics. You can explain it simply and list the characteristics.

Answer: Descriptive statistics describe data properties, and the five-number summary is the most common descriptive statistic in use. The common characteristics are:

  • Central tendency or middle of data: mean, median, and mode

  • Spread or dispersion of data: range, interquartile range, standard deviation, and variance

  • Shape or assessing the symmetry or skew: symmetric, left-skewed, and right-skewed

  • Outlier or abnormal value

What can you do with outliers?

This is to evaluate how well you understand errors in statistics and whether you know how to resolve them. Answer this question by defining outliers and discussing how to address them.

Answer: Outliers are abnormal values or are at an abnormal distance from the rest of the data points in a sample. There are two ways to deal with outliers:

  • Remove the outlier: when we know the data is incorrect, or have a lot of data, or can provide two different computations, one with an outlier and one without

  • Keep the outlier: when there are too many outliers, when the results are critical, or when outliers get meaning

What is an inlier?

This is another question to assess your ability to identify and address errors in datasets. Make sure to distinguish it from outliers.

Answer: Inliers are data observations within the dataset that are unusual or have errors. They differ from outliers because they lie inside the dataset or sample, making them harder to locate. When you identify an inlier, simply removing it is the best way to resolve them.

Define cherry-picking, p-hacking, and significance chasing

This question aims to assess your ability to identify biased behaviors and practices in statistics. Try to explain these concepts in simple terms using everyday language.

Answer: Cherry-picking is when one selects data supporting the desired conclusion and disregards other results or evidence. P-hacking refers to the practice of manipulating the analysis to the point that a non-significant result becomes significant. One way to achieve this is to stop collecting or using data mid-testing. Significance chasing is when the statistician refers to any insignificant results as if they are ‘nearly' significant.

What are the different types of sampling methods?

This is a fundamental statistic question to assess your knowledge of sampling. Simply list and explain its different types.

Answer: There are four key sampling methods:

  • Simple sampling: purely random sampling

  • Systematic sampling: sampling every nth member of the data set

  • Cluster sampling: sampling by dividing the population into groups or clusters

  • Stratified sampling: dividing the population by groups or strata and sampling from each group

Explain the meaning and significance of the empirical rule

This question aims to understand whether you are aware of a basic statistical rule. Explain the empirical rule in simple terms to answer this.

Answer: As per the empirical rule, in a normally distributed dataset, 68% of the data has to fall within one standard deviation, 95% of the data will fall within two standard deviations, and three standard deviations will contain 99.7% of all the data.

Define the concept of autocorrelation

Such questions evaluate your knowledge of concepts and principles that dictate large datasets and relationships between different data points. Explain the concept of autocorrelation and one of its features.

Answer: The degree of correlation between two variables represents autocorrelation in any given time series. This essentially means that the data has a connection, or correlation, in a manner that all future results have a link to past outcomes. Generally, autocorrelation can make a data model less accurate as the errors in the past results can flow in a sequential pattern into future outcomes.

If you have two fair dice, what is the probability of getting a sum of scores equal to four and eight?

These types of questions assess your ability to apply theoretical concepts. Solve the problem step-by-step to demonstrate your knowledge.

Answer: First, determine the total number of possible combinations between the two dice. This is 6*6 = 36.

To find the answer, first determine the combinations that can result in a sum of 4: (1+3, 3+1, 2+2)

So, the probability of rolling a four is 3/36 or 1/12.

Then, find the combinations of rolling the combination that can result in a sum of 8: (2+6, 6+2, 3+5, 5+3, 4+4)

So, the probability of rolling an eight is 5/36.

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Sample Interview Questions On Statistics

Here are some sample interview questions on statistics:

Easy statistical interview questions

The following are easy-to-answer questions related to statistics:

  • Define confidence interval in non-technical terms.

  • What is the significance of the p-value?

  • What are the drawbacks of A/B testing?

  • Explain the difference between mean, median, and mode.

  • Explain the Central Limit Theorem with an example.

  • Describe the different types of sampling in statistics.

  • How can you detect overfitting while creating a statistical model?

  • Explain survivorship bias in simple terms to a layperson.

  • Explain the relationship between standard deviation and standard variance.

  • If you roll three dice, one by one, what is the chance that you get three numbers in increasing order?

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Statistics questions with medium difficulty

Here are some questions with a medium-difficult level:

  • How many times do you have to flip a coin to get two consecutive heads?

  • Calculate the expectation for a geometric distributed random variable.

  • How would you find the mean size of all the fishes that exist in the sea?

  • Explain the concept and application of the TF/IDF vectorization.

  • Explain the concept of degree of freedom.

  • Discuss when you would use t-distribution and z-distribution.

  • Explain the assumptions necessary for linear aggression.

  • Give an example of a data set that has a non-Gaussian distribution.

  • How to calculate range and interquartile range?

  • What is Bessel's correction?

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Difficult statistics questions

Here are some tough statistics questions:

  • Explain the difference between MLE and MAP.

  • How can you randomly sample a point uniformly from a circle that has one as its radius?

  • What is the critical value when conducting a one-tail or two-tail test?

  • Explain the utility of Hash tables.

  • Define the law of large numbers.

  • Name a few low and high-bias machine learning algorithms.

  • How would you explain the concept of interpolation and extrapolation to a layperson?

  • Describe a situation where the median is a better measure than the mean.

  • The number of crimes in a city reduced from 115 to 99 over the last year. Is this change significant?

  • Define the Design of Experiments.

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Tips For Answering Statistics Interview Questions

Here are tips and best practices to follow while answering interview questions related to statistics:

  • Ask for clarifications. If you are asked the question verbally, you can ask the interviewer to repeat the details or ask for clarifications. Many practical statistics questions may require additional information as well.

  • Request a pen and paper. If the question requires complex calculations, you can ask for a pen and paper to solve them. This also allows the interviewer to review your approach to the problem.

  • Define formulas and concepts in simple terms. When answering questions related to formulas and models, try to balance the technical terms with everyday language. This can demonstrate your ability to understand these concepts well.

  • Revise and practice well. It is imperative to revise all basic statistical concepts and models before the interview. Practice different kinds of questions and problems to ensure you can easily solve questions during the interview.

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