MCQs

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Overall MCQs for Reference

1. What does “statistical thinking” primarily emphasize in machine learning?
   a. Data collection only
   b. Application of fixed rules
   c. Understanding data variability and uncertainty
   d. Ignoring patterns in data
   Answer: cmachinelearningmastery+1​
2. Which statistical measure is most resistant to outliers?
   a. Mean
   b. Median
   c. Mode
   d. Range
   Answer: bibm​
3. Which type of analysis helps summarize and describe the main features of a dataset?
   a. Predictive analysis
   b. Prescriptive analysis
   c. Descriptive statistics
   d. Inferential statistics
   Answer: clinkedin+1​
4. Which concept allows us to infer properties about a population from a sample?
   a. Descriptive statistics
   b. Inferential statistics
   c. Central tendency
   d. Regression
   Answer: bmachinelearningmastery+1​
5. Which measure quantifies the amount of spread in a dataset?
   a. Mean
   b. Standard deviation
   c. Mode
   d. Sample size
   Answer: bgeeksforgeeks​
6. The Central Limit Theorem states that as sample size increases:
   a. The means of samples will follow a normal distribution
   b. Outliers become more prominent
   c. Variance decreases to zero
   d. Data becomes categorical
   Answer: acoursera+1​
7. In statistical modeling, reducing model complexity can help avoid:
   a. Underfitting
   b. Overfitting
   c. Randomness
   d. Multicollinearity
   Answer: bmachinelearningmastery​
8. Which method estimates unknown parameters by maximizing the likelihood function?
   a. Minimum squared error
   b. Maximum likelihood estimation
   c. Mean absolute error
   d. Bayesian inference
   Answer: bcoursera+1​
9. In hypothesis testing, what does the p-value represent?
   a. The probability of a sample being biased
   b. The probability of observing data as extreme as the test statistic under the null hypothesis
   c. The mean of the data
   d. The maximum possible error
   Answer: bdatacamp+1​
10. What statistical method is commonly used for feature selection through dimensionality reduction?
    a. Linear regression
    b. Logistic regression
    c. Principal Component Analysis (PCA)
    d. K-means clustering
    Answer: cmachinelearningmastery​
11. Which of the following is NOT a measure of central tendency?
    a. Mean
    b. Median
    c. Mode
    d. Variance
    Answer: dibm+1​
12. What is the term for the error between predicted and actual output values in regression?
    a. Outlier
    b. Residual
    c. Feature
    d. Parameter
    Answer: bgeeksforgeeks+1​
13. Which distribution is appropriate for binary classification modeling?
    a. Normal
    b. Bernoulli
    c. Poisson
    d. Uniform
    Answer: bibm​
14. In a normal distribution, what percentage of data falls within one standard deviation of the mean?
    a. About 50%
    b. About 68%
    c. About 95%
    d. About 99%
    Answer: bgeeksforgeeks+1​
15. Which method adjusts for possible overfitting in model estimation?
    a. Cross-validation
    b. Feature scaling
    c. Mean normalization
    d. Range adjustment
    Answer: acoursera+1​
16. Kurtosis in a dataset measures:
    a. Spread
    b. Center
    c. Peakedness
    d. Symmetry
    Answer: cgeeksforgeeks​
17. Which value represents the likelihood of a specific event in probability theory?
    a. Variance
    b. Probability
    c. Confidence interval
    d. Mode
    Answer: bw3schools​
18. What is the purpose of an A/B test?
    a. To boost sample size
    b. To compare two groups or treatments
    c. To calculate mean variance
    d. To reduce overfitting
    Answer: bcoursera+1​
19. In Bayesian statistics, what is the “prior”?
    a. Future evidence
    b. Current evidence
    c. Pre-existing belief or distribution
    d. Sample mean
    Answer: ccoursera​
20. What does “skewness” measure in a data set?
    a. Variability
    b. Symmetry of the distribution
    c. Central value
    d. Spread around the mean
    Answer: bgeeksforgeeks​
21. Which type of plot helps visualize the relationship between two variables?
    a. Boxplot
    b. Scatter plot
    c. Histogram
    d. Pie chart
    Answer: blinkedin​
22. Which statistical measure is used to quantify relationships between two features?
    a. Covariance
    b. Mode
    c. Kurtosis
    d. Interquartile range
    Answer: aibm+1​
23. What does “variance” measure?
    a. Maximum value
    b. How far values spread from the mean
    c. The number of samples
    d. Central position
    Answer: bibm+1​
24. Which sampling method ensures each member of the population has an equal chance of selection?
    a. Stratified sampling
    b. Cluster sampling
    c. Random sampling
    d. Convenience sampling
    Answer: ccoursera​
25. Which metric is commonly used to evaluate regression model accuracy?
    a. Mean Squared Error (MSE)
    b. Gini index
    c. Entropy
    d. F1-score
    Answer: adatacamp+1​
26. The Law of Large Numbers states:
    a. Larger samples yield less reliable results
    b. Sample means approach the population mean as sample size increases
    c. Model accuracy decreases with large samples
    d. Feature importance is reduced with larger samples
    Answer: bmachinelearningmastery+1​
27. What is the usual assumption about residuals in linear regression?
    a. Uniform distribution
    b. Gaussian (normal) distribution
    c. Poisson distribution
    d. Binomial distribution
    Answer: bibm​
28. Which inferential statistic estimates the plausible range for a parameter?
    a. Median interval
    b. Confidence interval
    c. Maximum likelihood
    d. Mode interval
    Answer: bdatacamp+1​
29. Which loss function is standard in logistic regression?
    a. Mean squared error
    b. Absolute loss
    c. Cross-entropy loss
    d. Hinge loss
    Answer: cibm​
30. What is the first step in statistical thinking for a new machine learning dataset?
    a. Apply regression immediately
    b. Interpret the model output
    c. Explore and summarize the data
    d. Disregard missing values
    Answer: cmachinelearningmastery+1​
31. What test is used to assess independence between categorical features?
    a. T-test
    b. Chi-square test
    c. Z-test
    d. ANOVA
    Answer: bdatacamp​
32. Multicollinearity refers to:
    a. Non-normal data
    b. Highly correlated predictor variables
    c. Low variance features
    d. Categorical output variables
    Answer: bmachinelearningmastery+1​
33. What is regularization used for in machine learning models?
    a. Increase training error
    b. Prevent overfitting
    c. Boost sample size
    d. Minimize loss
    Answer: bcoursera+1​
34. The main role of probability in machine learning is to:
    a. Ignore uncertainty
    b. Quantify uncertainty and variability
    c. Force deterministic predictions
    d. Standardize all inputs
    Answer: blinkedin+1​
35. Which is NOT a common statistical distribution used in ML modeling?
    a. Binomial
    b. Poisson
    c. Uniform
    d. Fibonacci
    Answer: dmachinelearningmastery+1​
36. What does “bias” mean in model estimation?
    a. Random error only
    b. Systematic deviation from the true value
    c. Center of the distribution
    d. Feature variability
    Answer: bstatisticalhorizons​
37. What is the main benefit of stratified sampling?
    a. Simpler calculations
    b. Representation of key subgroups
    c. Uniform data spread
    d. Larger sample size
    Answer: bcoursera​
38. What statistic describes “peakedness” of a data distribution?
    a. Variance
    b. Skewness
    c. Kurtosis
    d. Range
    Answer: cgeeksforgeeks​
39. Which method tests whether means of two groups are statistically different?
    a. ANOVA
    b. Chi-square test
    c. T-test
    d. Kolmogorov-Smirnov test
    Answer: cdatacamp+1​
40. In Bayesian inference, the “posterior” combines prior beliefs with:
    a. Sample size
    b. New data (likelihood)
    c. Residuals
    d. Error estimates
    Answer: bcoursera​
41. Which is a measure of model goodness-of-fit in regression?
    a. R-squared
    b. Confusion matrix
    c. p-value
    d. Entropy
    Answer: adatacamp​
42. What is the main purpose of a hypothesis test?
    a. Predict missing data
    b. Assess evidence against a null hypothesis
    c. Find median value
    d. Increase sample size
    Answer: bquantinsti+1​
43. What is the meaning of “sample” in statistics?
    a. Entire population
    b. Subset representing the population
    c. Model feature
    d. Just outliers
    Answer: bquantinsti+1​
44. Which term describes categorical data with no inherent order?
    a. Ordinal
    b. Interval
    c. Nominal
    d. Discrete
    Answer: clinkedin+1​
45. What characterizes a positively skewed distribution?
    a. Mean < median < mode
    b. Mean > median > mode
    c. Mean < mode < median
    d. Mean = median = mode
    Answer: bgeeksforgeeks​
46. What is “correlation” used for?
    a. Predicting labels
    b. Measuring association between variables
    c. Calculating mean
    d. Testing independence
    Answer: bibm+1​
47. Which type of regression output is categorical?
    a. Linear regression
    b. Lasso regression
    c. Logistic regression
    d. Ridge regression
    Answer: cibm​
48. Which metric quantifies spread around the median?
    a. Variance
    b. Interquartile Range (IQR)
    c. Standard deviation
    d. Mean absolute error
    Answer: bgeeksforgeeks​
49. What does “statistical inference” involve?
    a. Estimating population parameters based on sample data
    b. Calculating means directly
    c. Training every model
    d. Applying only non-parametric tests
    Answer: alinkedin+1​
50. What is the main foundation of statistical machine learning?
    a. Purely computational algorithms
    b. Use of statistical principles for inference and prediction
    c. Ignoring sample size
    d. Not validating results
    Answer: bmachinelearningmastery+1​

1. https://machinelearningmastery.com/the-7-statistical-concepts-you-need-to-succeed-as-a-machine-learning-engineer/
2. https://blog.quantinsti.com/statistical-thinking/
3. https://www.ibm.com/think/topics/statistical-machine-learning
4. https://www.linkedin.com/pulse/statistics-machine-learning-key-concepts-you-should-know-yadav-e9p2c
5. https://www.geeksforgeeks.org/machine-learning/statistics-for-machine-learning/
6. https://www.coursera.org/learn/machine-learning-probability-and-statistics
7. https://www.datacamp.com/tutorial/unveiling-the-magic-of-statistical-machine-learning
8. https://www.w3schools.com/ai/ai_statistics.asp
9. https://statisticalhorizons.com/the-statistical-foundations-of-machine-learning/
10. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
11. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
12. https://itfeature.com/ds/ml/machine-learning-mcqs-10/
13. https://www.interviewbit.com/machine-learning-mcq/
14. https://es.scribd.com/document/378751974/MCQs-Machine-Learning
15. https://www.sanfoundry.com/machine-learning-questions-answers-statistical-learning-framework/
16. https://www.placementpreparation.io/mcq/machine-learning/
17. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
18. https://en.wikipedia.org/wiki/Machine_learning
19. https://interviewkickstart.com/blogs/articles/data-science-mcq-answers
20. https://www.sciencedirect.com/topics/engineering/statistical-thinking