Top 10 Statistics Questions Asked in Data Science Interviews With Examples
Almost assuredly, when you're prepping for your first data science interview or brushing up for one at an advanced level, statistics will be an issue. That is, interviewers want to assess both your theoretical understanding of statistics and your ability to apply statistical thinking to problems using real data.
In this post, we will consider the 10 statistics problems asked by most top interview questions in data science with examples and answer tips for standing out.
1. What Is the Difference Between Population and Sample?
Why is this asked: To measure your knowledge on the foundational building blocks of statistical inference?
Sample answer:
A population is the totality of the group that we are interested in studying (e.g., all customers of an e-commerce platform). A sample is a subset of that population that is to be studied in order to make conclusions about the whole.
Example: If we want to know the average spend of customers on Amazon, surveying 1,000 users (sample) gives us an argument for the average for all Amazon users (population).
2. What Is the Central Limit Theorem (CLT)? Why Is It Important?
Too frequently, the question is raised to ascertain whether the candidate has an understanding of a fundamental assumption of statistical inference.
Sample answer:
The CLT states that, regardless of the population distribution itself, the sample means become normally distributed when the samples are sufficiently large (usually n>30).
Importance: This allows us to use normal distribution techniques (for example, on confidence intervals and hypothesis tests) even if the underlying data are not normally distributed.
3. What’s the Difference Between Type I and Type II Errors?
That was an inquiry based on how much knowledge you have about hypothesis testing:
Type I error (false positive): acceptance of the null hypothesis when it is rejected by the test.
Type II error (false negative): acceptance of the null hypothesis when it is true.
Example: In spam detection, the type I error is that a legitimate email is detected as spam, while type II error is a spam email missing detection and coming to the inbox.
It was a question based on how much you know about hypothesis testing:
Type I error (false positive): acceptance of the null hypothesis when rejected by the test. Type II error (false negative): acceptance of the null hypothesis when it was true.
Cases: In spam detection, type I occurs when a legitimate email is detected as spam. Type II: A spam email is missed and lands in the inbox.
4. What is a p-value, and What Does It Represent?
Why it's asked: It will test your understanding of what statistical significance is.
Sample answer:
A p-value is the probability of obtaining results comparable to those observed under the null hypothesis. A small p-value (usually less than 0.05) suggests the evidence is strong enough to reject the null hypothesis.
5. What Is the Difference Between Parametric and Non-Parametric Tests?
The point is to assess the knowledge on the alternatives for the tests in question. For instance, in the case of a response:
Since they assume an underlying statistical distribution, the parametric tests (t-tests are, for instance, assumed to be normal).
On the other hand, non-parametric tests do not assume a particular distribution (for example, Mann-Whitney U test).
Non-parametric tests are performed for the data that does not meet the parametric assumptions.
6. Explain Confidence Intervals
The answer consists of a set of values between which it is expected that the true parameter of the population will fall, with a certain degree of confidence, usually 95% in this case.
For example: "We are 95% confident that the average height of students falls between 5.5 and 5.9 feet."
7. What Is the Difference Between Correlation and Causation?
We want to assess your ability to understand and interpret statistical relationships properly.
Sample Answer:
Correlation means the association between the states of the two random variables pertaining to both items under consideration.
Causation means one affects the other, whereby the change in one variable brings about a change in the other variable.
For instance, high summer temperatures increase both ice-cream sales and incidences of drowning; therefore, such instances are correlated, but neither cause the other-to speak of causation.
8. What Is Statistical Power and Why Does It Matter?
Explain why: to evaluate how well you understand the sensitivity of hypothesis testing.
A full answer: Power refers to the probability of correctly rejecting a false null hypothesis (i.e., detecting the true effect).
The higher the power, the lower the chances of making a Type II error. Factors influencing power include sample size, effect size, and significance level.
9. What Are Some Common Distributions Used in Statistics?
The rationale behind the asking: To see if you can match distributions with real-world applications.
Sample answer:
Normal Distribution: Heights, test scores
Binomial Distribution: Coin flips.
Poisson Distribution: Number of customer calls per hour.
Exponential Distribution: Time between failures in systems.
It is key, however, to know when each distribution applies in terms of both modelling and inference.
10. How Would You Handle Outliers in a Dataset?
The reason for this inquiry: To assess your application of practice in cleaning and analyzing data.
Examples of possible answers: Discus what has caused the outlier: mistake or true extreme value?
Your options:
Remove it if it really evidenced an error.
Transform the data by log transformation, etc..
Utilize robust modeling which is not sensitive to outliers, e.g., median-based metrics.
Never remove outliers blindly; contextualize first.
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Final words
Statistics is the backbone of Data Science. If you are good at concepts of Statistics like inferencing, testing and distributions, you will be much ahead in interviews.
Interview prep tips: Do not memorize the formula; rather understand why and when to use it.
Use real life examples in your answers.
Come and join Softronix for more detailed information about the topic. Our professionas are here to sort out your problems and available at your convenience.
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