**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

**Probability Sampling:**Here, each member of the target population has a known chance of being included in the sample.**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.

Learn more about __Azure Data Engineer Interview Questions__ here.

**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.