1.
Introduction
2.
Statistics Interview Questions for Freshers
2.1.
1. What is sampling?
2.2.
2. Define some data sampling techniques.
2.3.
3. What is Data Collection in statistics?
2.4.
4. What is the difference between population and sample in Inferential Statistics?
2.5.
5. What do you mean by Sampling error?
2.6.
6. What is Central Limit Theorem? State the condition for the Central Limit Theorem.
2.7.
7. What does Six Sigma represent in statistics?
2.8.
8. What is skewness in statistics?
2.9.
9. What is a mode, and when is it used in statistical analysis?
2.10.
10. What is variability, and what are the measures to calculate it?
2.11.
11. Define Poisson distribution.
3.
Intermediate Statistics Interview Questions
3.1.
12. What exactly is kurtosis?
3.2.
13. Explain the difference between Correlation and Causation.
3.3.
14. What do you mean by inlier?
3.4.
15. What is multistage sampling?
3.5.
16. What exactly is the chi-square distribution?
3.6.
17. What is a regression model in statistics?
3.7.
18. How is the error calculated in a linear regression model?
3.8.
19. What is a t-test, and when should we use it?
3.9.
20. What distinguishes a paired t-test from a one-sample t-test?
3.10.
21. What use do hash tables serve in statistics?
4.
4.1.
22. What is the significance of outliers in statistics?
4.2.
23. How does the chi-square distribution change as the degree of freedom (k) increases?
4.3.
24. What assumptions are required for linear regression?
4.4.
25. When should you use Fisher's exact test and McNemar's test?
4.5.
26. How are one-way and two-way ANOVAs different from one another?
4.6.
27. What do you mean by Effect size, and what is the significance of effect size in statistics?
4.7.
28. What is missing data, and what are the methods for cleaning up missing data?
4.8.
29. What is a critical value in data science?
4.9.
30. State and define the three error metrics for a linear regression model.
4.10.
31. With an example, explain the impact of seasonality on a time-series model.
5.
5.1.
How do I prepare for a statistics interview?
5.2.
What are good statistical questions?
5.3.
What to expect in a statistician interview?
5.4.
What are the 4 principles of statistics?
6.
Conclusion
Last Updated: Jun 14, 2024
Medium

# Top 75 Statistics Interview Questions & Answers 2023

Vikash Kumar
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

## Introduction

The role of statistics in data science and data analytics cannot be overstated. Statistics provides tools and methods for determining the structure and providing deeper insights into data. Statistics with facts to educated guesses and knowing the principles of Statistics will enable you to think critically and creatively when solving business problems and making data-driven decisions.

Here is a list of the top statistics interview questions with proper answers. It will help you refresh your memory on crucial parts of statistics and prepare for Data Science job interviews.

## Statistics Interview Questions for Freshers

Let's see some easy-level statistics interview questions.

### 1. What is sampling?

Ans. A sample is a subset of an entire population. Sampling is the process of identifying the group from which you will collect for your research. For example, if you are researching the opinions of students in your school about academics, you could survey a sample of 50 students. The goal of sampling is to create a sample that represents the entire population.

### 2. Define some data sampling techniques.

Ans. Sampling techniques can be broadly classified into two types

1. Probability Sampling: Here, each member of the target population has a known chance of being included in the sample.
2. Non-Probability Sampling: It involves selecting a sample based on non-random criteria, and not every member of the population has an equal chance of being included.

Under probability sampling, we have four data sampling techniques.

• Simple random sampling
• Stratified sampling
• Systematic sampling
• Convenience sampling

Under Non-Probability Sampling also, there are four techniques.

•  Convenience sampling
• Purposive sampling
• Voluntary response sampling
• Snowball sampling

### 3. What is Data Collection in statistics?

Ans. Data collection is the systematic gathering of observations or measurements. Data gathering allows you to get first-hand information and fresh insights into your study challenge, whether you are conducting research for business, government, or academic objectives.

### 4. What is the difference between population and sample in Inferential Statistics?

Ans. A population is a whole group about which you want to draw conclusions.

A sample is a refined group from which you will collect data. The sample size is always less than the total population size. Using the sample, we calculate the statistics. And we draw conclusions about the population using these sample statistics.

### 5. What do you mean by Sampling error?

Ans. The difference between a population parameter and a sample statistic is called a sampling error. Sampling errors occur even when a random sample is used. This is because random samples are not exactly equivalent to the population in terms of means and standard deviations.

### 6. What is Central Limit Theorem? State the condition for the Central Limit Theorem.

Ans. The central limit theorem, abbreviated as CLT, is a statistical theory that states that when a large sample size has a finite variance, the samples will be normally distributed, and the mean of samples will be approximately equal to the mean of the whole population.

According to the central limit theorem(CLT), the sampling distribution of the mean will always follow a normal distribution if the following conditions satisfy

• The sample size is large.
• The random variables used in the samples are independent and identically distributed.
• The population's distribution has a finite variance.

### 7. What does Six Sigma represent in statistics?

Ans. Six Sigma is a statistical quality control methodology that produces error-free data sets. Sigma is another name for standard deviation. The greater the standard deviation, the less likely that the process will function accurately and result in a defect.

### 8. What is skewness in statistics?

Ans. Skewness measures the lack of symmetry in a data distribution. It indicates that there are significant differences between the mode, the mean, and the median of data. Skewed data cannot be used to create a normal distribution.

### 9. What is a mode, and when is it used in statistical analysis?

Ans. A data set's mode or modal value is the most often occurring value. It is a measure of central tendency that indicates the most popular option or most common feature in your sample. The mode is most useful with categorical data. It is the only measure of central tendency for nominal variables that can indicate the most frequently observed feature (e.g., demographic information).

### 10. What is variability, and what are the measures to calculate it?

Ans. Variability describes how far apart data points are from each other and the center of the distribution. Measures of variability, like measures of central tendency, provide descriptive statistics that summarise your data. Variability can be measured using the range, interquartile range, standard deviation, and variance.

### 11. Define Poisson distribution.

Ans. A Poisson distribution is a discrete probability distribution that predicts the probability of a discrete (countable) outcome. The discrete result of a Poisson distribution is the number of times an event occurs, denoted by k. A Poisson distribution can be used to predict or explain the number of events that occur within a specific time or space interval.

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## Intermediate Statistics Interview Questions

Here are some medium-level statistics interview questions and answers.

### 12. What exactly is kurtosis?

Ans. Kurtosis describes the extreme values that exist in one tail of a distribution against the other. It is a measure of the number of outliers in the distribution. A high kurtosis number indicates that there are a lot of outliers in the data. To address this, we must either add more data to the dataset or remove outliers.

### 13. Explain the difference between Correlation and Causation.

Ans. Correlation describes the relationship between two types of variables: when one changes, so does the other. Correlation is a statistical measure to calculate the extent of the relationship between two or more variables. But causation is the process through which changes in one variable induce changes in the other; there is a cause-and-effect relationship between variables.

### 14. What do you mean by inlier?

Ans. An inlier is a data point in a data set that is on the same level as the other data points. It is typically an error that is removed to increase model accuracy. In contrast to outliers, inliers are difficult to locate and may require external data for identification.

### 15. What is multistage sampling?

Ans. Multistage sampling, also known as multistage cluster sampling, involves selecting a sample from a population in smaller and smaller groups at each stage. For example, this strategy is frequently employed to collect data from a large, geographically dispersed group of people.

You can also check Angular interview questions here.

### 16. What exactly is the chi-square distribution?

Ans. These are a type of continuous probability distribution. They're used mostly in hypothesis testing, such as the independence tests and chi-square goodness of fit. The degrees of freedom(k) determines the shape of a chi-square distribution. The range of the chi-square distribution is from 0 to infinity.

### 17. What is a regression model in statistics?

Ans. A regression model is a model that uses a line to represent the connection between a dependent variable and multiple independent variables. A plane is used to represent the connection in case of two or more independent variables.

The dependent variable can be quantitative when using a regression model, with the exception of logistic regression, which uses a binary dependent variable.

### 18. How is the error calculated in a linear regression model?

Ans. Linear regression often calculates model error using mean-square error (MSE). MSE is determined by:

• At each value of x, the difference between the observed and estimated y-values is measured.
• Each of these distances is squared, and the mean of these squared distances is calculated.
• By identifying the regression coefficient that produces the smallest MSE, linear regression finds a line that fits the data.

### 19. What is a t-test, and when should we use it?

Ans. A t-test is a test used to compare the means of two groups. It is frequently used in hypothesis testing to assess whether a procedure or treatment has an impact on the population of interest or whether two groups differ.

A t-test can only be used to compare two groups' means (pairwise comparison). When comparing more than two groups or making multiple pairwise comparisons, utilize an ANOVA test or a posthoc test.

### 20. What distinguishes a paired t-test from a one-sample t-test?

Ans. A one-sample t-test compares a single population to a reference value (for example, to find whether the average lifespan of a specific city is different from the country average).

A paired t-test compares two populations before and after an experimental intervention or at two separate points in time (for example, evaluating children's performance in an exam before and after being taught a subject).

### 21. What use do hash tables serve in statistics?

Ans. A systematic representation of key-value pairs is shown by hash tables, which are a type of data structure. A hash table uses the hashing algorithm to create an index that contains all of the information about the keys that are mapped to their corresponding values.

Now, we will see some hard-level statistics interview questions.

### 22. What is the significance of outliers in statistics?

Ans. Outliers in statistics have a significant negative effect since they skew the outcome of any statistical query. For example, if we want to determine the mean of a dataset, including outliers, the calculated mean will differ from the actual mean.

### 23. How does the chi-square distribution change as the degree of freedom (k) increases?

Ans. The shape of the chi-square distribution changes from a descending slope to a hump when the degrees of freedom (k) is increased. As k(degree of freedom) increases, the hump shifts from substantially right-skewed to almost normal.

### 24. What assumptions are required for linear regression?

Ans. There are four main assumptions for linear regression.

• The dependent variables and the regressors have a linear relationship, so the model we are building truly fits the data.
• The data errors or residuals are regularly distributed and independent of one another.
• There is little multicollinearity among explanatory factors.
• Homoscedasticity: This signifies that the variance around the regression line is the same for all predictor variable values.

### 25. When should you use Fisher's exact test and McNemar's test?

Ans. Fisher's exact test is a preferable choice if you have a small sample size (N<100). When your data does not meet the criteria of a minimum of five observations predicted in each combined group, you should use Fisher's exact test.

But, when you have a closely linked pair of categorical variables with two groups, you should utilize McNemar's test. It enables you to see if the proportions of the variables are equal.

### 26. How are one-way and two-way ANOVAs different from one another?

Ans. The sole difference between one-way and two-way ANOVA is the number of independent variables. A one-way ANOVA has one independent variable, whereas a two-way ANOVA has two independent variables.

One-way ANOVA: Investigating the association between shoe brands (Nike, Adidas, Saucony, and Hoka) and marathon race finish timings.

Two-way ANOVA: Investigating the link between shoe brands (Nike, Adidas, Saucony, Hoka), runner age category (junior, senior, master's), and marathon finishing times.

### 27. What do you mean by Effect size, and what is the significance of effect size in statistics?

Ans. Effect size indicates how significant the relationship between variables or the difference between groups is. It denotes the practical importance of a research finding.

A large effect size suggests that a study discovery has practical significance, whereas a small effect size indicates that the research finding has limited practical applications.

### 28. What is missing data, and what are the methods for cleaning up missing data?

Ans. Missing data, also known as missing values, occur when data for certain variables or participants is not stored. There is always some missing data in any dataset. Missing values in quantitative research appear as blank cells in your spreadsheet.

Accepting, eliminating, or generating missing data are standard methods for cleaning up missing data.

• Acceptance: Leaving the data as it is.
• Listwise or pairwise deletion: Removing all cases (participants) from analyses that have missing data.
• Imputation: filling in missing data with additional data.

### 29. What is a critical value in data science?

Ans. A critical value is a value that establishes the upper bounds and lower bounds of a confidence interval or the statistical significance threshold in a statistical test. It specifies how far out from the distribution's mean you must travel to cover a specific percentage of the total variation.

### 30. State and define the three error metrics for a linear regression model.

Ans. The MSE, RMSE, and MAE are the three error measures most frequently used to monitor performance.

MSE: the mean squared error (MSE). Represents the difference between the original and predicted values extracted by squaring the average difference across all data points.

RMSE: The square root of the MSE is used to get the RMSE (root mean squared error). It is the error rate by the square root of MSE.

MAE: It represents the difference between the original and predicted values extracted by averaging the absolute difference across all data points.

### 31. With an example, explain the impact of seasonality on a time-series model.

Ans. Seasonality is an important feature to consider when developing a time-series model. These cycles repeat over time and must be accounted for in the model being created.

Assume you want to create a model that estimates the number of hoodies sold in the next few months. If you simply use data from the beginning of the year to construct the prediction and ignore the prior year, you will fail to account for seasonal variations in purchasing patterns. People would buy fewer hoodies in March and April than they did in February since the weather is growing warmer, something the machine learning algorithm does not account for.

### How do I prepare for a statistics interview?

The best way to prepare for a statistics interview is to clear the concepts of statistics and be confident. Next, you should go through the previously asked statistics interview questions for practice. Also, be clear and concise in your answers in the interview.

### What are good statistical questions?

Good statistical questions are the ones that can be answered by collecting and analyzing varying data. Like, given the data on the height of employees, asking the average height of employees is a good statistical question instead of asking that of a particular employee.

### What to expect in a statistician interview?

In a statistician interview, you should expect questions to be asked ranging from the technical knowledge required, your analytical abilities, and personal experience.

### What are the 4 principles of statistics?

The four principles of statistics are validity, reliability, bias, and variability. These four altogether when followed, gives optimal result in computations.

## Conclusion

In this article, we discussed the most-asked statistics interview questions and answers in data science interviews. Hope this article helped you to get a proper understanding of the type of questions asked in statistics interview questions. You can also learn some numerical methods to solve statistical problems here

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