Statistics interview questions and answers (Part 1)
Tags: datascience, statistics, interview
Published: 2021-10-28 13:50:53

## These are some questions that are related to statistics


>1\. Where you have used Hypothesis Testing in your machine learning solution?

Answer: Hypothesis testing is one of the statistical analysis where we test the assumption made    for any particular situation. While testing some assumption which was claimed to be true I performed the hypothesis testing where the null hypothesis was that whatever claimed results to be true and the alternate hypothesis was that whatever claim was made was false.

>2\. What do you understand by P-value? And what is the use of it in machine learning?

Answer: P-value which is also know as probability value, it the probability of null hupothesis to be true. It sets the rule to reject null hypothesis.

If the p-value is less than the significance value then we reject the null hypothesis or else accept it.

If the p-value falls in the 95% of the confidence interval then we will accept the null hypothesis.

>3\. Which type of error is sever error, Type1 or Type2? And why with example.

Answer: To answer of this question is it depends. It depends on the problem statement we are looking into.

For example:

The confusion matrix with regards to disease vs treatment is fatal in case of **false negative** (when patient have the disease and the model predicted that patient dont have the disease) then in that case patient wont get the treatment and might loose his/her life.

Similarly in criminal is guilty or innocent case **false positive** is is much more worse (when the person is inncent and the model predicts person is guilty) as we will end up punishing an innocent.

>4\. Can we use Chi-Squared with numerical dataset? If yes, give example. If no, give reason.

Answer: Chi-Squared generally deals with categorical data rather than only numerical data.

Chi Sqauared finds the differences or it compares two or more groups with a value, or to compare two or more groups.

>5\. What do you understand by ANOVA testing?

Answer: ANOVA stands for Analysis of Variance. It is an extenstion of T-Test.

In T-Test we test if there is any difference in mean and it can only test of two groups at a time, so if there are more than 2 groups instead of performing T-Test multiple times we go for ANOVA testing.

ANOVA testing looks for two different types of variations:

* variation within groups
* variation between groups
To test ANOVA our hypothesis will be:

null hypothesis: There is no difference in means
alternate hypothesis: Atleast or mean differs from the other means
There are two type of ANOVA:

* One way ANOVA
* Two way ANOVA

One way ANOVA: When we want to test two groups and see if thee is any difference.

Two way ANOVA: When we test twice for the same group.

>6\. Give me a scenario where we can use the Z-test and T-test.

Answer: We use either of the test depending on:

<u>Sample size</u>: 

When sample size is large or greater than 30 we use Z-test else T-test

<u>Population variance</u>:

When population variance is known, we use Z-test else T-test

<u>Distribution</u>:

If normally distributed we perform Z-test else T-test

>7\. What do you underdstand by inferential statistics?

Answer: When we try to form a conclusion about the population by conducting the experiments on sample taken from the population.

>8\. When you are trying to calculate standard deviation or variance, why you used n-1 in denominator?

Answer: Having denominator as n-1 corrects the biasness in the estimation of the population variance.

So, for example:

* if we have data points where the population mean is not inside the sample points
* now, if we take the sample mean and the distance between the sample points to the sample mean, that will be much lower estimate as compared to population variance
* this could lead to underestimating the population variance
* hence by dividing the denominator by n-1 which makes the denominator much smaller and in turn gives high value for the sample variance which will be unbiased estimate'

>9\. What to do you understand by right skewness? Give example.

Answer: When the data is not normally distributed and we have tail type elongated line on the right side, that is called right skewness.

For example:

Income distrubution.

![Income distrubution](https://www.mathsisfun.com/data/images/skewed-distribution.svg)



>10\. What is the the difference between normal distribution, standard normal distribution and uniform distribution?

Answer:

<u>Normal distribution</u>

* it is a density curve which is a bell shaped curve
* has the tendency of the data to cluster around the central value which is also known as population mean
* has a total area of 100%

<u>Standard normal distribution</u>

* it is a special type of normal distribution which has the mean as 0 and the standard deviation as 1

<u>Uniform distribution</u>

* this distribution has the values which lies between certain range/boundary