10 Statistics Interview Questions for Data Science Role

A data science interview is likely to include questions on statistics, given that it is an integral part of the role. Reviewing common statistics interview questions is a good way to prepare for your interview. 

Because data science has grown in popularity, making it a realistic option for your future career, you want to prepare for your job interview to make the most of your opportunity in an expanding industry generating large sums of money. According to Fortune Business Insights, the global big data analytics market was valued at $307.51 billion in 2023 and is projected to grow from $348.21 billion in 2024 to $924.39 billion by 2032 [1].

Read further to explore 10 statistical questions in data science for job interviews in this field to familiarise yourself with and practice answering, as well as some general data science interview tips.

Statistics interview questions for data science

Any data science interview will include questions on statistics, as it is such a central element of the data scientist role. Statistics underpins your abilities to collect, store, analyse, and make sense of the data you work with. Below are 10 examples of statistics-related questions in data science that may come up in an interview.

1. Tell us about yourself.

What they’re really asking: What makes you the right candidate for this job?

This is a common interview question across industries, not just data science. Employers want to hear a little bit about you and how that relates to your ability to do the job. They want to hear about your motivations, personal attributes, experience, successes, and how well you’ve researched the company and position. Think about how your previous experience and skills brought you to this interview and answer the question in the context of what you can do for the company. You can even directly connect the responsibilities at your previous job to those in the job description for this new position. 

Other forms this question might take:

• I’d love to hear more about your journey.

• Tell me a little bit more about your background.

• Tell me something about yourself that’s not on your resume.

2. What is statistics?

What they’re really asking: How much do you know about statistics and how it relates to data science?

With this question a recruiter is testing you on your statistical knowledge. Break down what statistics is, and link it to data science and the job you’re interviewing for. Use examples from your personal experience to fully demonstrate your knowledge, including technical terminology such as analysis, interpretation, cluster sampling, outliers, and statistical models. 

Other forms this question might take:

• What is your experience with statistics?

• How do you use statistics?

• Define statistics.

3. Describe the statistical significance of hypothesis testing.

What they are really asking: What is your knowledge and experience of hypothesis testing?

With this question, a recruiter wants to know your knowledge about hypothesis testing, how you would use it, and your experience. Talk about the hypothesis and null hypothesis and how you prove it, and mention the p-value as the calculation determining the significance of the statistical insight. If you can walk the interviewer through an actual example of this overall process, you can fully illustrate your understanding.

Other forms this question might take:

• What is a hypothesis test?

• How is the statistical significance of an insight determined?

• How do you assess the statistical significance of an insight?

4. Tell us what you understand about Six Sigma.

What they are really asking: Are you experienced in Six Sigma?

A question like this is your opportunity to talk about Six Sigma based on your experience. Give a definition and talk about projects you’ve worked on where you used Six Sigma as a quality control method, explaining how you implemented the first five iterations of Sigma until you reached the sixth. Make sure to mention that your goal was to produce almost error-free work. Finally, if you have earned your Six Sigma certification, you can also share this.

Other forms this question might take:

• What is Six Sigma in statistics?

• Tell us about your experience with Six Sigma.

5. Explain what an outlier is and how it can be determined in a data set.

What they are really asking: What is your process for characterising data?

With this question, the recruiter assesses your skills in characterising data and determining outliers within data sets. They want to learn about your methods, accuracy, and the tools and processes you use to ensure you don’t have bad data in your set because leaving an outlier in the data set can skew the precision and efficiency of the statistical model. Your answers tell the recruiter about your attention to detail and problem-solving methods.

Other forms this question might take:

• Describe a time you have determined outliers in a data set.

• What do you do if you discover an outlier?

6. Explain what a KPI is in statistical terms.

What they are really asking: How do you track your performance?

With a question about KPIs (key performance indicators), recruiters want to know your ability to set and meet goals and targets, the KPI measures, and whether you have met them. Approach this question by demonstrating your knowledge of KPIs in statistical terms and give examples from your experience in which you achieved the predetermined goals.

Other forms this question might take:

• How do you use KPIs?

• Give me an example of a KPI in statistical terms.

• What is a KPI?

7. Is a large data set always best?

What they are really asking: Do you assume a large data set is always better than a smaller one?

With a question like this, the recruiter seeks a balanced answer, considering the benefits and pitfalls of a large data set. The interviewer likely wants to discover if you know the value in these larger sets while ensuring you know that larger data sets cost more to analyse and can contain bias. They test your understanding and see how you put together a well-thought-out argument. Use real examples according to your experience.

Other forms this question might take:

• What are the benefits of a large data set?

• What are the advantages of a small data set?

8. Can you explain the difference between a sample and a population?

What they’re really asking: Do you know the difference between key statistical terms?

When recruiters ask this question or similar, they want to test your basic understanding of statistical terms and whether you can tell the difference between items, such as sample and population, that data science experts use together. Comprehending these two terms' differences is a crucial aspect of data science. Explain that a sample is a subset of a population and how this relates to statistical research and analysis, meaning that the information gained from the sample can assist you in learning something about the entire population.

Other forms this question might take:

• How does a sample relate to a population?

• How does a sample represent a population as a whole?

9. What statistical tools do you plan to use as a data scientist?

What they are really asking: How do you validate your decisions? 

The tools you use to solve a problem say a lot about your methods and processes. Explain the tools, such as programming languages or the Tableau platform, in depth to show your knowledge, and use examples to demonstrate your experience with tools, why you chose those in particular, and the results of doing so.

Other forms this question might take:

• What experience do you have with statistical tools?

• How do you use statistical tools in your work as a data scientist?

10. Explain how you would apply root cause analysis to a real-life situation

What they’re really asking: How do you put what you know in theory into practice?

Employers are looking at your problem-solving abilities and how you apply your knowledge to real-life situations. Include a definition of root cause analysis, and give examples of how it applies to something real and tangible. Also, you can explain the difference between correlation and causation with root cause analysis. For maximum points, tie it in with something relevant to the company or give examples from your previous work. 

Other forms this question might take:

• When have you used root cause analysis?

• Provide an example of root cause analysis. 

Tips for preparing for your data science interview 

Reviewing data science questions in various forms is an excellent way of preparing for your interview and building your confidence. In addition to this, consider the following data science interview tips:

• Research the company: Know everything you can about the company you’re interviewing with so you can align your answers with their values and mission, as well as make sure everything you say covers the job criteria. Additionally, if you’re told who will be conducting the interview, you can try to learn this person’s role at the company, their specialisation, and their contribution to the business, which will help you seem more prepared.   

• Practice: Reviewing common interview questions is helpful, but you can take it one step further and practice some interviews. Ask a friend, colleague, or family member to ask you some questions and practice answering them in a mock interview, making sure to speak clearly and purposefully. You might even consider requesting feedback from this person about your answers. 

• Prepare some questions: An interview isn’t just about answering questions. You’ll have an opportunity to ask your interviewer some questions. Prepare some questions that demonstrate your interest in the company, job progression, and highlights and challenges of the role. Having specific questions for the interviewer is another way to show them you are serious about the position. 

Getting started with Coursera

Researching and preparing for statistics questions before your data science job interview can help you confidently walk into the room. With plenty of practice beforehand, you can seriously improve your chances of getting hired for the job.

As you prepare for a job interview in data science, you might consider increasing your knowledge and skills to bolster your CV with an online course. Look at the IBM Data Science Professional Certificate or the Google Data Analytics Professional Certificate, both listed on Coursera.