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I am a data scientist with a background in computer science.

I am familiar with data structures, object-oriented programming, and database management because I learned these concepts in college for three years.

However, when I entered the data science field, I noticed there was a huge skills shortage.

I didn’t have the mathematical or statistical background required for almost every data science position.

I took a few online statistics courses, but none of them really seemed to stick.

Most of the programs were either very basic and geared towards top managers, others were detailed and built on prerequisite knowledge that I did not possess.

I’ve spent a lot of time scouring the internet for resources to better understand concepts like hypothesis testing and confidence intervals.

After interviewing for several data science jobs, I found that most statistics interview questions followed a similar pattern.

In this article, I will discuss the 10 most popular statistics questions I have encountered during data scientist interviews, and provide sample answers to these questions.

Question 1: What is a p-value?

Answer: Assuming the null hypothesis is true, a p-value is the probability that you will see a result that is at least as extreme as the observed result.

P-values ​​are typically calculated to determine whether the result of a statistical test is significant. Simply put, the p-value tells us whether there is sufficient evidence to reject the null hypothesis.

Question 2: Explain the concept of statistical power

Answer: When you perform a statistical test to determine whether there is an effect, statistical power is the probability that the test will accurately detect the effect.

Here’s a simple example to explain this:

Let’s say we run an ad for a test group of 100 people and get 80 conversions.

The null hypothesis is that the ad had no effect on the number of conversions. In reality, the ad did have a significant impact on the number of sales.

Statistical power is the probability that you would correctly reject the null hypothesis and actually detect the effect. Higher statistical power indicates that the test is better able to detect an effect if there is one.

Question 3: How would you describe confidence intervals to a non-technical stakeholder?

Let’s use the same example as before, where an ad is shown to a sample of 100 people and 80 conversions are obtained.

Instead of saying the conversion rate is 80%, we would give a range, because we don’t know how the real population would behave. In other words, if we took an infinite number of samples, how many conversions would we see?

Here’s an example of what we could say, based solely on the data from our sample:

“If we were to run this ad in front of a larger group of people, we are 95% confident that the conversion rate would be somewhere between 75% and 88%.”

We use this range because we don’t know how the total population will respond. We can only make an estimate based on our test group, which is just a sample.

Question 4: What is the difference between a parametric and a nonparametric test?

A parametric test assumes that the data set follows an underlying distribution. The most common assumption when performing a parametric test is that the data is normally distributed.

Examples of parametric tests are ANOVA, T-test, F-test and the chi-square test.

However, nonparametric tests do not make any assumptions about the distribution of the data set. If your data set is not normally distributed, or if it contains ranges or outliers, it is wise to choose a nonparametric test.

Question 5: What is the difference between covariance and correlation?

Covariance measures the direction of the linear relationship between variables. Correlation measures the strength and direction of this relationship.

Although correlation and covariance provide similar information about the relationship between traits, the key difference is scale.

Correlation ranges from -1 to +1. It is standardized and allows you to easily understand whether there is a positive or negative relationship between characteristics and how strong this effect is. On the other hand, covariance is shown in the same units as the dependent and independent variables, which can make it a bit more difficult to interpret.

Question 6: How would you analyze and handle outliers in a dataset?

There are a number of ways to detect outliers in the dataset.

• Visual Methods: Outliers can be identified visually using graphs such as box plots and scatter plots. Points that fall outside the whiskers of a box plot are typically outliers. When using scatter plots, outliers can be detected as points that are far away from other data points in the visualization.
• Non-visual methods: A non-visual technique to detect outliers is the Z-Score. Z-Scores are calculated by subtracting a value from the mean and dividing by the standard deviation. This tells us how many standard deviations a value is from the mean. Values ​​that are above or below 3 standard deviations from the mean are considered outliers.

Question 7: Distinguish between a one-tailed and a two-tailed test.

A one-tailed test checks for a relationship or effect in one direction. For example, after running an ad, you can use a one-tailed test to check for a positive impact, i.e. an increase in sales. This is a right-tailed test.

A two-tailed test examines the possibility of a relationship in both directions. For example, if a new teaching style was implemented in all public schools, a two-tailed test would assess whether there was a significant increase or decrease in scores.

Question 8: Which statistical test would you implement in the following scenario?

An online retailer wants to evaluate the effectiveness of a new advertising campaign. They collect daily sales data for 30 days before and after the ad was launched. The company wants to determine if the ad contributed to a significant difference in daily sales.

Options:
A) Chi-square test
B) Paired t-test
C) One-way ANOVA
d) Independent samples t-test

AnswerTo evaluate the effectiveness of a new advertising campaign, we need to use a paired t-test.
A paired t-test is used to compare the means of two samples and check whether a difference is statistically significant.
In this case, we are comparing sales figures before and after the ad, and comparing a change in the same group of data. Therefore, we use a paired t-test instead of an independent samples t-test.

Question 9: What is a Chi-square test for independence?

A Chi-square test of independence is used to examine the relationship between observed and expected outcomes. The null hypothesis (H0) of this test is that any observed difference between the characteristics is due to chance alone.

Simply put, this test can help us determine whether the relationship between two categorical variables is due to chance, or whether there is a statistically significant association between the two.

For example, if you want to test whether there is a relationship between gender (male vs. female) and ice cream flavor preference (vanilla vs. chocolate), you can use a chi-square test for independence.

Question 10: Explain the concept of regularization in regression models.

Regularization is a technique used to reduce overfitting by adding extra information. This allows models to better adapt and generalize to datasets they were not trained on.

In regression, two commonly used regularization techniques are used: ridge and lasso regression.

These are models that slightly modify the error equation of the regression model by adding a penalty term.

In ridge regression, a penalty term is multiplied by the sum of the squared coefficients. This means that models with larger coefficients are penalized more. In lasso regression, a penalty term is multiplied by the sum of the absolute coefficients.

Although the main goal of both methods is to reduce the size of the coefficients while minimizing the model error, large coefficients are penalized more in ridge regression.

In contrast, lasso regression applies a constant penalty to each coefficient, meaning that the coefficients can drop to zero in some cases.

10 Statistical Questions to Ace Your Data Science Interview — Next Steps

If you managed to follow this far, congratulations!

You now have a good understanding of the statistical questions asked in data science job interviews.

As a next step, I recommend taking an online course to refresh your knowledge of these concepts and put them into practice.

Here are some statistics learning resources that I have found useful:

The latter course can be taken for free on edX, while the first two resources are YouTube channels that go into depth on statistics and machine learning.

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Natasha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes about everything related to data science, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.