In a Data Science interview, candidates are often assessed on their knowledge of statistics, as statistics is a fundamental component of data analysis and modelling. If you want to know what kind of interview questions based on statistics you can get in a Data Science interview, this article is for you. In this article, I’ll take you through some of the most important and common Data Science interview questions based on statistics you should know.
Data Science Interview Questions on Statistics
Let’s go through some common Data Science interview questions based on statistics.
What is Descriptive Statistics?
Descriptive statistics is a branch of statistics that deals with summarizing and describing key characteristics of a dataset. It includes measures such as mean, median, mode, variance, and standard deviation.
Descriptive statistics provide a concise overview of the dataset’s central tendencies and variability.
Explain the Central Limit Theorem.
The Central Limit Theorem is a fundamental concept in statistics. It states that when you repeatedly draw random samples from a population and calculate the means of these samples, the distribution of those means will be approximately normal, regardless of the population’s underlying distribution.
This theorem is essential for making inferences about a population based on sample data.
What is Hypothesis Testing, and why is it important?
Hypothesis testing is a statistical method used to make decisions or inferences about a population based on sample data. It involves formulating a null hypothesis (typically a statement of no effect) and an alternative hypothesis, collecting data, and using statistical tests to determine whether there is enough evidence to reject the null hypothesis.
Hypothesis testing is crucial for making data-driven decisions, conducting experiments, and validating assumptions.
What is p-value in Hypothesis Testing?
The p-value is a measure that quantifies the strength of evidence against the null hypothesis. It represents the probability of observing a test statistic as extreme as, or more extreme than, what was observed, assuming the null hypothesis is true.
A smaller p-value suggests stronger evidence against the null hypothesis, leading to its potential rejection.
Differentiate between Type I and Type II Errors in Hypothesis Testing.
In hypothesis testing, a Type I error occurs when the null hypothesis is incorrectly rejected when it is, in fact, true. A Type II error happens when the null hypothesis is not rejected when it is false.
These errors represent the trade-off in hypothesis testing: minimizing one type of error often increases the risk of the other.
What is Linear Regression, and how is it used?
Linear Regression is a statistical method used to model the relationship between a dependent variable (target) and one or more independent variables (features) by fitting a linear equation. It is widely used for tasks like predicting numeric outcomes based on input features.
The goal is to find the best-fitting linear equation that explains the relationship between variables.
Explain Overfitting and Underfitting.
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and random fluctuations rather than the underlying patterns. It leads to poor generalization to new, unseen data. Underfitting, on the other hand, happens when a model is too simplistic to capture the underlying patterns in the data.
Balancing between these two extremes is crucial to building effective predictive models.
So, these are just a few examples of Data Science interview questions based on statistics. In a Data Science interview, candidates are often assessed on their knowledge of statistics, as statistics is a fundamental component of data analysis and modelling. I hope you liked this article on Data Science interview questions based on statistics. Feel free to ask valuable questions in the comments section below.