Statistics Concepts You Must Know for Data Science Interviews
๐ 1. Descriptive Statistics
These help summarize and understand the data.
Key Concepts:
Mean, Median, Mode
Variance, Standard Deviation
Range, IQR (Interquartile Range)
Skewness and Kurtosis
Percentiles and Quartiles
Interview Example:
“How would you describe a skewed distribution?”
“What’s more robust to outliers: mean or median?”
๐ 2. Probability Fundamentals
Understanding randomness and uncertainty.
Key Concepts:
Independent vs Dependent Events
Mutually Exclusive Events
Conditional Probability
Bayes’ Theorem
Law of Total Probability
Combinatorics (e.g. permutations & combinations)
Interview Example:
“What is the probability of flipping 3 heads in a row?”
“Explain Bayes’ Theorem with an example.”
๐งช 3. Probability Distributions
You must know common distributions and when to use them.
Discrete:
Bernoulli
Binomial
Poisson
Continuous:
Normal (Gaussian)
Exponential
Uniform
Concepts:
PDF, PMF, CDF
Expected Value
Variance of Distributions
Interview Example:
“When would you use a Poisson distribution?”
“Why is the normal distribution so commonly used in statistics?”
๐ง 4. Inferential Statistics
Drawing conclusions from data samples.
Key Concepts:
Population vs Sample
Sampling Methods (random, stratified, etc.)
Central Limit Theorem
Confidence Intervals
Margin of Error
Z-scores and t-scores
Interview Example:
“Why is the Central Limit Theorem important?”
“What does a 95% confidence interval mean?”
๐ 5. Hypothesis Testing
Crucial for A/B testing and experimentation.
Key Concepts:
Null and Alternative Hypotheses
P-value
Statistical Significance
Type I and Type II Errors
Power of a Test
One-tailed vs Two-tailed Tests
Z-test, t-test, ANOVA, Chi-Square Test
Interview Example:
“What is a p-value, and how do you interpret it?”
“What’s the difference between Type I and Type II errors?”
๐ 6. Correlation & Regression
Understanding relationships between variables.
Key Concepts:
Correlation vs Causation
Pearson & Spearman Correlation
Simple and Multiple Linear Regression
R-squared and Adjusted R-squared
Assumptions of Linear Regression
Multicollinearity
Homoscedasticity
Interview Example:
“What does R² tell you about a regression model?”
“What happens when predictors are highly correlated?”
⚠️ 7. Bias & Variance Tradeoff
Key in understanding model performance.
Concepts:
Overfitting vs Underfitting
High Bias, High Variance
Regularization (L1/L2)
Interview Example:
“What is the bias-variance tradeoff?”
“How can you reduce overfitting?”
๐งช 8. Experimental Design & A/B Testing
Frequently asked in product data science roles.
Key Concepts:
Control vs Treatment
Randomization
Sample Size Calculation
Significance Level (ฮฑ)
Effect Size
Power Analysis
Lift Calculation
Interview Example:
“How would you design an A/B test for a new feature?”
“What would you do if your A/B test results were inconclusive?”
๐งฐ 9. Real-World Statistical Thinking
How you apply stats in practical problems.
Examples:
Dealing with missing or noisy data
Understanding data distributions before modeling
Choosing the right metrics for evaluation
Communicating statistical results clearly
๐ Resources to Study:
Books:
“Practical Statistics for Data Scientists” by Bruce & Gedeck
“The Art of Statistics” by David Spiegelhalter
Courses:
Khan Academy Statistics
StatQuest with Josh Starmer (YouTube)
Practice:
DataLemur
LeetCode Stats Questions
StrataScratch
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