MCQs

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Overall MCQs for Reference

What is the primary goal of hypothesis testing in artificial intelligence?
a. To prove a theory correct
b. To draw conclusions about a population based on sample data
c. To increase model complexity
d. To maximize overfitting
Answer: b. To draw conclusions about a population based on sample dataappliedaicourse+1
What is the null hypothesis (H₀) typically associated with in hypothesis testing?
a. Existence of an effect
b. No significant effect or relationship
c. Maximizing model accuracy
d. Unsupervised learning
Answer: b. No significant effect or relationshipdatacamp+1
In the context of hypothesis testing, what does the alternative hypothesis (H₁) represent?
a. The status quo
b. The presence of a significant effect or relationship
c. Lack of significant finding
d. Random sampling
Answer: b. The presence of a significant effect or relationshipgeeksforgeeks+1
Which of the following best describes a p-value?
a. The probability of the null hypothesis being true
b. The probability of observing the data, or more extreme, assuming H₀ is true
c. The sample mean
d. The maximum likelihood
Answer: b. The probability of observing the data, or more extreme, assuming H₀ is truesimplilearn+1
What decision is made if the p-value is less than the significance level (α)?
a. Accept the null hypothesis
b. Reject the alternative hypothesis
c. Reject the null hypothesis
d. Increase the sample size
Answer: c. Reject the null hypothesisscribbr+1
What does a Type I error indicate?
a. Failing to reject H₀ when it is false
b. Rejecting H₀ when it is true
c. Accepting H₁ when it is true
d. Failing to reject H₁ when it is true
Answer: b. Rejecting H₀ when it is truescribd+1
What does a Type II error indicate?
a. Failing to reject H₀ when it is false
b. Rejecting H₁ when it is false
c. Failing to reject H₀ when it is true
d. Accepting H₁ when it is false
Answer: a. Failing to reject H₀ when it is falsescribd+1
In which situation is a two-tailed test used?
a. Only when the hypothesis predicts an increase
b. Only when the hypothesis predicts a decrease
c. When testing for any difference, regardless of direction
d. When comparing medians
Answer: c. When testing for any difference, regardless of directionscribd+1
Which test is suitable for comparing means across more than two independent groups?
a. T-test
b. ANOVA
c. Chi-square test
d. Z-test
Answer: b. ANOVAdatamites+1
What does the significance level (α) represent in hypothesis testing?
a. The maximum risk of making a Type II error
b. The probability threshold to reject H₀
c. The minimum sample size needed
d. The effect size
Answer: b. The probability threshold to reject H₀testbook+1
What is meant by “statistical power” in hypothesis testing?
a. The ability to accept H₀ when true
b. The ability to detect an effect, if present
c. The p-value magnitude
d. The number of tails in a test
Answer: b. The ability to detect an effect, if presentsimplilearn+1
Which of the following is NOT an assumption for t-tests?
a. Normality of the data
b. Independence of observations
c. Homogeneity of variance (for two-sample t-test)
d. Data must be categorical
Answer: d. Data must be categoricaldatamites+1
A right-tailed test is appropriate when seeking evidence for which hypothesis?
a. H₀: µ ≤ µ₀, H₁: µ > µ₀
b. H₀: µ = µ₀, H₁: µ ≠ µ₀
c. H₀: µ ≥ µ₀, H₁: µ < µ₀
d. H₀: µ = µ₀, H₁: µ = µ₀
Answer: a. H₀: µ ≤ µ₀, H₁: µ > µ₀savemyexams+1
What is the function of the test statistic in hypothesis testing?
a. To estimate population size
b. To summarize data for hypothesis assessment
c. To visualize data only
d. To generate random samples
Answer: b. To summarize data for hypothesis assessmentscribbr+1
Which of the following is used to measure the goodness of fit for categorical variables?
a. T-test
b. ANOVA
c. Chi-square test
d. Z-test
Answer: c. Chi-square testsavemyexams+1
When using a significance level of 0.01, what does it mean?
a. The probability of a Type I error is 1%
b. The probability of a Type II error is 1%
c. The sample size is 1% of the population
d. Results are always significant
Answer: a. The probability of a Type I error is 1%testbook+1
What does the term “critical value” represent in hypothesis testing?
a. The mean of the population
b. The threshold that separates the acceptance and rejection regions
c. The median value of a sample
d. The variance of the sample
Answer: b. The threshold that separates the acceptance and rejection regionstestbook+1
Which is required before performing a hypothesis test?
a. Definition of null and alternative hypotheses
b. Drawing a histogram
c. Collecting only categorical data
d. Calculating correlation coefficient
Answer: a. Definition of null and alternative hypothesestestbook+1
What kind of sample is required for valid hypothesis testing?
a. Biased sample
b. Convenience sample only
c. Random or representative sample
d. Only large samples
Answer: c. Random or representative samplesimplilearn+1
The error of accepting a false null hypothesis is called:
a. Type I error
b. Type II error
c. Alpha error
d. Beta error
Answer: b. Type II errorscribd+1
The sample mean minus the population mean, divided by the standard error, gives:
a. The Z-score or t-statistic
b. The p-value
c. The median
d. The mode
Answer: a. The Z-score or t-statisticdatacamp+1
What is the main use of hypothesis testing in machine learning?
a. Hyperparameter tuning
b. Model validation and comparison
c. Feature selection
d. Boosting only
Answer: b. Model validation and comparisontestrigor+1
In AI-driven A/B testing, hypothesis testing is used to:
a. Compare user engagement metrics between two groups
b. Optimize hardware architecture
c. Encrypt user data
d. Reduce code size
Answer: a. Compare user engagement metrics between two groupsappliedaicourse+1
In the context of AI, why is controlling for Type I error important?
a. To maximize CPU usage
b. To avoid claiming false improvements in algorithms
c. To always accept H₀
d. To use larger datasets
Answer: b. To avoid claiming false improvements in algorithmstestrigor+1
Which test is most suited for a small sample (n < 30) with unknown variance?
a. Z-test
b. T-test
c. Chi-square test
d. F-test
Answer: b. T-testdatamites+1
In AI, what is a “one-sample t-test” commonly used for?
a. Comparing means between two samples
b. Testing if the mean of a group differs from a known value
c. Evaluating variance of populations
d. Only comparing medians
Answer: b. Testing if the mean of a group differs from a known valuedatacamp+1
Which assumption must be met for an independent two-sample t-test?
a. Categorical outcome
b. Independent samples
c. Unequal variances
d. Data drawn from different types of distributions
Answer: b. Independent samplesdatamites+1
What is the main reason to use a nonparametric hypothesis test?
a. When data is binary
b. When data does not meet parametric assumptions
c. For larger datasets
d. To choose random forests
Answer: b. When data does not meet parametric assumptionssimplilearn+1
Which of the following best explains the role of hypothesis testing in AI fairness?
a. Maximizing algorithm speed
b. Controlling for data bias in model evaluation
c. Optimizing image compression
d. Feature scaling
Answer: b. Controlling for data bias in model evaluationappliedaicourse+1
What is the confidence interval related to in hypothesis testing?
a. Model architecture
b. Range likely to contain the true parameter value
c. Number of epochs
d. Type II error probability
Answer: b. Range likely to contain the true parameter valuedatacamp+1
Which test is used to compare the variances of two populations?
a. T-test
b. F-test
c. ANOVA
d. Z-test
Answer: b. F-testdatamites+1
If the p-value is greater than α, what is the correct decision?
a. Reject H₀
b. Fail to reject H₀
c. Accept H₁ unconditionally
d. Always repeat experiment
Answer: b. Fail to reject H₀scribbr+1
Which is an example of a one-tailed hypothesis in ML model evaluation?
a. H₁: Model A performs better than Model B
b. H₁: Model A and Model B perform differently
c. H₀: Both models perform equally
d. H₀: Model A performs worse than Model B
Answer: a. H₁: Model A performs better than Model Bsavemyexams+1
What does ‘statistically significant’ mean in hypothesis testing?
a. The result has a large magnitude
b. The observed effect is unlikely under H₀, given chosen α
c. The sample size is maximum
d. The population mean is reached
Answer: b. The observed effect is unlikely under H₀, given chosen αsimplilearn+1
To test if feature selection improves a model, which hypothesis setup is correct?
a. H₀: Feature selection has no effect
b. H₁: Feature selection improves the model
c. Both a and b
d. None of the above
Answer: c. Both a and btestrigor+1
What type of variable does the chi-square test analyze?
a. Numeric only
b. Categorical
c. Continuous
d. Ordinal only
Answer: b. Categoricalsavemyexams+1
What is the main risk of conducting multiple hypothesis tests?
a. Decreased computational speed
b. Increased overall risk of Type I error
c. Always accepting H₀
d. Need for smaller datasets
Answer: b. Increased overall risk of Type I errordatacamp+1
What is the “decision rule” in hypothesis testing?
a. The rule for calculating mean
b. The criterion for rejecting or not rejecting H₀
c. The formula for variance
d. The stopping criterion for an algorithm
Answer: b. The criterion for rejecting or not rejecting H₀testbook+1
What is required for a paired t-test?
a. Two independent groups
b. Two related (paired) groups
c. Group means must be equal
d. Data are nominal
Answer: b. Two related (paired) groupsdatamites+1
In what scenario would non-inferiority testing be used in AI?
a. When verifying an algorithm is better than baseline
b. Testing an algorithm is not worse than standard
c. Comparing sample sizes
d. When α is unknown
Answer: b. Testing an algorithm is not worse than standardappliedaicourse+1
When should you use a Z-test in AI hypothesis testing?
a. When the population variance is unknown and sample size is small
b. When the population variance is known and sample size is large
c. Only when data is binary
d. For feature encoding
Answer: b. When the population variance is known and sample size is largesimplilearn+1
What does it mean if a hypothesis test has high power?
a. High likelihood of Type I error
b. High likelihood of detecting a true effect
c. Small p-value always
d. High significance level
Answer: b. High likelihood of detecting a true effectdatacamp+1
Why is hypothesis testing valuable for benchmarking AI algorithms?
a. Helps statistically verify differences between models
b. Produces faster code
c. Explains feature engineering
d. Guarantees overfitting
Answer: a. Helps statistically verify differences between modelsappliedaicourse+1
What is a one-tailed p-value?
a. Probability under both ends
b. Probability under only one direction of interest
c. Always equals to α
d. Only for categorical data
Answer: b. Probability under only one direction of interestscribd+1
Why is sample size important in hypothesis testing?
a. It controls variance estimation
b. Determines reliability and power of results
c. Required only for visualization
d. Is never important
Answer: b. Determines reliability and power of resultssimplilearn+1
What is the main output of hypothesis testing in AI model evaluation?
a. A confidence interval
b. Decision to reject or fail to reject H₀
c. The sample standard deviation
d. New algorithm selection
Answer: b. Decision to reject or fail to reject H₀datacamp+1
To establish a result as “not statistically significant,” what must be true?
a. The p-value is less than α
b. The p-value is greater than α
c. The mean is the same as variance
d. The z-score is maximum
Answer: b. The p-value is greater than αscribbr+1
In hypothesis testing, what role does replication play in AI research?
a. Reduces bias and confirms findings across datasets
b. Increases variance only
c. Lowers statistical power
d. Minimizes computational steps
Answer: a. Reduces bias and confirms findings across datasetstestrigor+1
What is the key assumption underlying many parametric hypothesis tests?
a. Data is always categorical
b. Data is normally distributed
c. Data is quantized
d. Distributions are never known
Answer: b. Data is normally distributeddatamites+1
In AI-driven business analytics, hypothesis testing most often supports:
a. Strategic decision-making based on data evidence
b. Writing source code
c. Data entry automation
d. Only hardware upgrades
Answer: a. Strategic decision-making based on data evidenceappliedaicourse+1

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