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Statistics

Q. Explain the different design methods used in A/B Testing
Q. Explain the difference between Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) Estimate
Q. What is the difference between Mean, Median and Mode?
Q. How to choose between mean and median to summarize data?
Q. What is a random variable?
Q. What is Bayes’ Rule?
Q. How does Bayesian Statistics differ from the Frequentist paradigm?
Q. What are the main components of a Bayesian Model?
Q. What is a probability function, and what properties must it satisfy?
Q. What is the difference between a Probability Mass Function (PMF), Probability Density Function (PDF), and Cumulative Distribution Function (CDF)?
Q. What is the difference between parametric and non-parametric models?
Q. What are the pros and cons of parametric vs. non-parametric models?
Q. What is the Central Limit Theorem (CLT), and what are its implications for statistical inference?
Q. What is Isolation Forest?
Q. What is a p-value, and what is its significance?
Q. What is a Confidence Interval?
Q. What is an Outlier?
Q. What are some options for dealing with outliers?
Q. What is a Z Score?
Q. What is Skewness and Kurtosis?
Q. What does it mean if observations are iid, and why is this a desirable property?
Q. What does it mean for two events to be independent?
Q. What does it mean for two events to be mutually exclusive?
Q. What is conditional probability?
Q. What is the difference between probability and likelihood?
Q. What is the relationship between independence and correlation?
Q. What is the difference between covariance and correlation?
Q. What are some automatic outlier detection mechanisms?
Q. What is Local Outlier Factor?
Q. What are some desirable properties of estimators?
Q. What is Chebyshev’s Theorem and its implications?
Q. What is the Empirical Rule?
Q. What is Kolmogorov–Smirnov statistic?
Q. What is the difference between a parameter and a statistic?

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Explore Questions by Topics

Computer Vision (1)
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Machine Learning Basics (18)
Deep Learning (52)
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  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Training and Optimization (17)
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Supervised Learning (115)
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  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
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  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (55)
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  - Distance Measures (9)
  - K-Means Clustering (9)
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  - Clustering Evaluations (6)
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Statistics (34)
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Other Questions in Statistics

Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
What is a Decision Tree? Explain the concept and working of a Decision tree model
What is Bagging? How do you perform bagging and what are its advantages?
Explain the concept and working of the Random Forest model
What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
What are the advantages and disadvantages of Decision Tree model?

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