 # 17 Most Common Statistician Interview Questions & Answers

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Computing and data analysis are both essential in helping businesses understand markets, consumer behavior, and relationships between variables. Today, almost every organization needs a statistician, making the role more competitive than ever. Knowing the questions to expect in a statistician interview can set you apart from other candidates.

The most common statistician interview question requires you to differentiate between observational and experimental data. Interviewers ask this question to test if you’re informed about the various methods of collecting data. Your response should be an explanation of each technique.

## 1. What’s the Difference Between Observational and Experimental Data?

Purpose: This is a basic question the employer uses to test your understanding of various ways of collecting statistical data. It also assesses whether you understand the level of confidence to place on each method.

Answer: Observational data results from observing how variables change and arriving at a conclusion without further manipulating the data. Examples include cohort study and case study data.

On the other hand, experimental data comes from various studies where the researcher introduces an intervention to study its effect. In other words, observational data requires no human intervention, while experimental data comes from human manipulation.

## 2. What Are Outliers in a Dataset, and How Would You Address Them?

Purpose: Though you would expect statistical data to have a normal variation, sometimes you may may get abnormal values that can significantly influence your statistical results. This question aims to test if you know what outliers are and how you deal with them.

Answer: Outliers are values that differ enormously from other values in a dataset. Some represent natural variations in a population, while others result from errors. Mostly, you detect outliers through data visualization, though there are other methods like sampling and interquartile range.

When you identify an outlier in your dataset, you can choose to remove or retain it. You keep it when it’s a natural value of your dataset and remove it when you have a legitimate reason it is an error. If you’re unsure if an outlier is an error, you should retain it to ensure your data is not biased and to prevent making a wrongful conclusion.

## 3. Name Three Types of Biases You Encounter as a Statistician

Purpose: It’s impossible to eliminate bias from statistical data. This question tests if you can recognize bias in a dataset and minimize it to get reliable results.

Answer: Bias is an error resulting from misrepresenting a parameter, leading to an inaccurate population representation. The various types of bias you’d encounter in statistics include:

• Selection bias: It arises when you fail to achieve proper randomization when selecting a population for data analysis.
• Sampling bias: It results from choosing a sample that does not represent the entire population.
• Omitted variable bias: This bias comes from leaving out relevant variables in a population you’re analyzing.

## 4. Explain the Pareto Principle

Purpose: This question tests whether you know there is no balance between input and output and if you can use this principle while analyzing statistical data.

Answer: The Pareto principle is an observation and not a law that states that 80% of consequences result from 20% of causes. The principle came from wealth and population analysis in Italy, which showed that 80% of Italian land is owned by 20% of the population. And a further survey in other countries resulted in the same observation.

It helps to identify variables that influence results the most compared to others. For example, it can help to determine which marketing efforts result in increased sales.

## 5. What Are the Four Most Important Skills for a Biostatistician?

Purpose: Interviewers ask this question to learn if you know what they expect from you.

Answer: The most essential skills for a biostatistician include:

• Data analysis to perform sound statistical analysis to arrive at unbiased conclusions.
• Critical thinking to be able to look past superficial indicators when making inferences about your analysis.
• Computer proficiency to simplify data collection, analysis, and presentation using computing systems and computer software.
• Programming to develop data processing and computing software.

## 6. When Is It Appropriate To Use Median Instead of Mean?

Purpose: Measure of central tendency is a familiar concept you must encounter in statistics. It helps you represent a data set using a single value. This question tests if you know where to apply mean and median when analyzing data.

Answer: Median is the most central number in a dataset, and you use it when there are outliers or skewed distribution. On the other hand, the mean is the average or the total of a dataset divided by the number. You use it when the distribution is symmetrical and there are no outliers.

## 7. Explain the Central Limit Theorem

Purpose: When dealing with a large population where n is greater than 30, you must use statistical theorems to save time when analyzing data. This question evaluates your knowledge of using sampling to represent a large dataset.

Answer: The central limit theorem states that the mean sample approximates a normal distribution as the population increases. Usually, the sample size considered sufficient in central limit theory is 30. And the sample’s standard deviation and mean will equal that of the whole population. This implies that a large sample size can approximate the characteristics of a population.

## 8. How Do You Test a Hypothesis?

Purpose: The key ingredient of executing a research study is knowing how to test a hypothesis. Answering this question tells the interviewer whether you know the steps to follow when researching.

1. Develop a research question to answer through data collection, analysis, and interpretation.
2. Formulate a null (Ho) and alternate (Ha) hypothesis. A null hypothesis is always negative and states there’s no relationship between the variables you’re testing. Conversely, an alternate hypothesis is positive and indicates a connection between the variables.
3. Collect sample data representing the population of your interest.
4. Perform statistical tests such as p-test to analyze variances within the variables.
5. Decide which hypothesis is correct. If the p-value is below 0.05, you accept the null hypothesis and reject the alternate hypothesis.
6. Summarize and present your findings.

## 9. What Does KPI Mean in Statistics?

Purpose: The interviewer uses this question to assess if you know how to measure operating goals.

Answer: KPIs or key performance indicators are parameters that show progress towards a set goal. For KPIs to be effective, you must set objectives and track implementation regularly. KPIs can be:

• Operational: They measure the day-to-day activities of an organization. They have the shortest timeframe.
• Lagging and leading: Lagging KPIs measure historical events, and leading KPIs future events.
• Strategic: They track long-term goals such as revenue growth.`

## 10. What Does Six Sigma in Statistics Mean?

Purpose: Interviewers ask this question to see if you understand measures of improving quality and eliminating errors in processes to boost profitability.

Answer: Six Sigma is a mathematical theory that uses statistical concepts to analyze data and reduce errors and defects. It concentrates on improving cycle time to maintain manufacturing errors below 3.4 per million units. All industries can use Six Sigma to meet customer demands, improve customer retention, and sustain production levels.

## 11. What Is the P-Value?

Purpose: You can’t complete statistical research without using the p-value. This question tests if you understand the significance of the p-value in statistics.

Answer: The p-value (probability value) is a number that describes how likely your data would occur by random chance. It lies between 0 and 1 and tells you whether you should accept or reject a null hypothesis. The higher the p-value, the stronger the evidence you should accept the null hypothesis.

If your p-value is less than 0.05, it shows that the probability the null hypothesis is incorrect is 95%. In this case, you should reject the null hypothesis and accept the alternate hypothesis. On the other hand, a p-value greater than 0.05 is insignificant statistically and indicates a high probability the null hypothesis is correct.

## 12. Explain How Type I and Type II Errors Differ

Purpose: Type I and II errors are unavoidable, but you can reduce them. If you can differentiate between the two errors, it shows the interviewer you can recognize them and use suitable measures to rectify them.

Answer: Type I error or alpha error occurs when you reject the null hypothesis when it’s actually correct. It gives a false positive by concluding the p-value is statistically significant. Type II error or beta error is a false negative that arises when you accept the null hypothesis when it’s actually wrong.

Though both errors can affect your conclusion, the type I error is more significant, and you should reduce it by lowering the significance level or the p-value. For type II errors, you should reduce them by increasing the sample size.

## 13. A Binomial Distribution Must Meet Certain Criteria. What Are They?

Purpose: When carrying out multiple surveys, distribution is vital to help you make inferences about the population. However, your conclusion will be incorrect if you fail to meet certain conditions. This question tests your ability to apply binomial distribution in statistical analysis.

Answer: Binomial distribution is the probability of failure or success outcome in a survey repeated multiple times. To use binomial distribution, your data must meet the following four conditions:

• Have a fixed number of trials: You must conduct all tests the same for you to obtain the probability.
• Independent trials: Each trial should not influence the outcome of the other to be able to use the multiplication rule, which involves multiplying the probabilities together.
• Exact probabilities: The probability of successful or unsuccessful trials must remain the same.
• Two outcomes: Each trial can either be a success or a failure.

## 14. When Do You Apply a t-Test and a z-Test?

Purpose: The t and z-test are the most common statistical tests to test the null hypothesis. This question aims to determine if you know when to apply each to your data.

Answer: You use the t-test when:

• The sample size is less than 30.
• Your data has a normal distribution.
• You don’t know the standard deviation of the population.

Z-test, on the other hand, applies when:

• The sample size is greater than 30.
• Your data has a normal distribution.
• When you have the standard deviation of the population.

## 15. What’s the Meaning of Symmetrical Distribution

Purpose: Data distribution is critical when analyzing and making inferences about a population. The interviewer can use this question to gauge your data analysis knowledge.

Answer: Symmetrical distribution refers to the ability to divide data into two equal halves that mirror each other. It borrows from the central limit theorem. The mean, mode, and median must be identical when data is symmetrical. Examples of symmetrical distribution include normal distribution, t-distribution, and Cauchy distribution.

## 16. What’s the Relationship Between Margin of Error and Standard of Error

Purpose: You cannot compute the margin of error without standard error. This question aims to assess if you know how to calculate the margin of error.

Answer: The margin of error is a percentage that shows how much your results differ from the actual population value. In contrast, the standard error is the standard deviation of a sample distribution. The relationship between the two is that margin of error is a product of critical value and standard deviation. To calculate the margin of error, you must calculate the standard error first.

Standard Error = critical value x standard error of the sample/population

## 17. Explain Two Sampling Methods Used in Biostatistics

Purpose: This question aims to assess the method you would use to collect data when the study population is large.

Answer: Sampling involves selecting a representative of a population for analysis. The various ways of collecting samples include:

• Symmetrical sampling: It involves sorting the population in order, for example, ascending or descending order, and selecting the samples to use for analysis randomly. Compared to other methods, the probability of error is negligible or low.
• Stratified sampling: It involves dividing the population into homogeneous subgroups or strata and randomly picking the strata to analyze.

## Sources Nathan Brunner