The field of data science is growing rapidly, and with it, the demand for qualified data scientists. If you’re looking to enter the data science industry or advance your career, it’s important to be prepared for interviews. In this blog, we have compiled a list of 101 statistics interview questions that cover a wide range of topics in statistics. These questions will help you prepare for your upcoming data science interview and increase your chances of success.

## Define statistics.

**Ans**: The study of statistics focuses on how to gather, arrange, examine, and understand numerical data and information.

## Why do we study Statistics?

**Ans**: You encounter statistics every day and they are all around you. Although statistics are truthful, statisticians may lie in any of the following circumstances:

- Data Gathering
- Data Understanding
- Data Analysis/Interpretation
- Data Presentation

## Define sample and population.

**Ans**: Complete Total data with respect to the problem statement is called population. The population is represented by the sample. Every characteristic of the population should be present in the sample. The sample is a subset of the population.

## Define census and survey.

**Ans**: Census is the term used to describe the collection of data from the entire population. The survey is the process of gathering information from the sample in order to draw conclusions about the population.

## What are the assumptions of data?

**Ans**: Following are the assumptions of data:

- Data have a normal distribution (Normality)
- A sample is random when each data point in your population has an equal chance of being included in the sample (Random samples).
- The value of one observation does not influence or affect the value of other observations (Statistical Independence).
- Different samples can come from populations with different means, they have the same variance (Equal Variance).
- A stable process is one in which the inputs and conditions are consistent over time. This means the sources of variation are consistent over time, and the process does not exhibit unpredictable variation. In contrast, if a process is unstable and changes over time, the sources of variation are inconsistent and unpredictable. As a result of the instability, you cannot be confident in your statistical test results. (Stability)

## What is a parameter?

**Ans**: It is a descriptive measure of the population.

For example, population mean, population variance, population standard deviation, etc.

Population Parameters:

Mean – μ

Variance – σ2

Standard Deviation – σ

## What is Statistic?

**Ans**: It is a descriptive measure of the sample.

For example, the sample mean, sample variance, sample standard deviation, etc.

Sample Statistic:

Mean – x

Variance – s2

Standard Deviation – s

## Define descriptive statistics and inferential statistics.

**Ans**: Descriptive Statistics – Data gathered about a group to reach conclusions about the same group.

Inferential Statistics – Data gathered from a sample and the statistics generated to reach conclusions about the population from which the sample is taken. Also known as Inductive Statistics.

## What is the difference between data analysis and machine learning?

**Ans**: Strong coding skills and a working understanding of statistics are required for data analysis. We employ machine learning techniques to solve issues whose answers cannot be formulated by utilising conventional rule-based algorithms. In addition to having a great understanding of statistics and business, machine learning demands some basic coding skills.

## What is Machine Learning?

**Ans**: Machine learning can be deﬁned as the process of solving a practical problem by

1) gathering a dataset, and

2) algorithmically building a statistical model based on that dataset.

## Conclusion

The questions provided in this blog will give you an idea of what to expect in a statistics interview and will help you prepare accordingly. While it’s important to study and memorize these questions, remember to also focus on the underlying concepts and principles behind them. With enough preparation and practice, you’ll be well on your way to acing your next statistics interview and landing your dream data science job.

Check out the table of contents for Product Management and Data Science to explore those topics.

Curious about how product managers can utilize Bhagwad Gita’s principles to tackle difficulties? Give this super short book a shot. This will certainly support my work.

AI is fun! Thanks a ton for exploring the AI universe by visiting this website.