How a potential employee answers this question can give an employer insight into their willingness and ability to learn new things. Since statisticians use logic and critical thinking to identify alternative solutions, a good answer should show the applicant is flexible and open to advanced ideas. What to look for in an answer:

• Enthusiasm about learning new concepts

• Critical thinking skills

• Clear knowledge of statistical concepts

Statisticians should be able to easily answer this question. An important aspect of a statistician's job is weighing out the pros and cons to alternative solutions, and data scientists consistently formulate hypothesis to determine the most logical conclusions. What to look for in an answer:

• Strong understanding of alternative hypothesis

• Open-mindedness

• Ability to demonstrate problem-solving skills

Inferential statistics is utilized by data miners, researchers and statisticians in a wide range of fields that include government operations and the corporate world. An applicant with experience in data science should be comfortable answering this question. What to look for in an answer:

• Clear knowledge of inferential statistics

• Critical thinking skills

• Research experience

There are three different types of biases that can occur when statisticians take small data samples from large populations during analysis procedures. An experienced data scientist would be able to name the three biases. What to look for in an answer:

• Knowledge of common biases

• Good research skills

• Clear demonstration of data analytical skills

Six Sigma is a method utilized by statisticians across multiple platforms in various industries to help improve overall function and processes when analyzing and working with data. This is a common method in which most statisticians, data miners and other data scientists should demonstrate clear knowledge. What to look for in an answer:

• Clear understanding of Six Sigma methodology

• Good decision-making skills

• Ability to apply critical thinking to processes

The five-number summary is essentially a measurement process for a specific range of data. Statisticians and data miners utilize this method to gain a clearer understanding of the overall distribution of data, so interviewees with experience in this process would be able to provide a concise definition. What to look for in an answer:

• Knowledge about the five-number summary

• Willingness and desire to learn new things

• Ability to demonstrate strong research skills

The Pareto principle is commonly used in statistics and other data science processes. Sometimes referred to as the 80/20 rule, the principle refers to experiments when 80% of the results are obtained through 20% of the experiment's causes. What to look for in an answer:

• Critical thinking skills

• Clear comprehension of the Pareto principle

• Strong analytical skills

This is an important question for interviewers to ask statisticians, as missing data is an issue that can occur in multiple processes. There are several options for handling missing data, and an experienced statistician would demonstrate that they're familiar with the processes. What to look for in an answer:

• Problem-solving and critical thinking skills

• Willingness to think outside the box to find the correct answer

• Good decision-making skills

How an interviewee answers this question provides insight into their knowledge and overall experience in conducting and observing various experiments. Data miners and statisticians with past experience can easily describe the two types of data. What to look for in an answer:

• Clear understanding of observational and experimental data

• Analytical thinking skills

• Applicant pays attention to detail

This question is a basic inquiry into an applicant's previous experience. How an interviewee answers can give an employer a good idea of how knowledgeable they are with various processes and provide insight on what their specific duties were at their last job. What to look for in an answer:

• Strong knowledge of statistics

• Programming and research skills

• Problem-solving and analytical thinking skills