MCQs

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Overall MCQs for Reference

Which statistical test is commonly used to determine if there is a significant difference between the means of two groups in AI research?
a. ANOVA
b. T-test
c. Regression
d. Chi-square
Answer: b. T-testgeeksforgeeks+2
What is the primary assumption to perform a t-test?
a. Data should be ordinal
b. Variance should be large
c. Data should be normally distributed
d. Population size must be large
Answer: c. Data should be normally distributedjulius+1
T-tests are commonly applied in AI to evaluate:
a. Model parameter tuning
b. Mean performance difference between models
c. Data normalization
d. Dimensionality reduction
Answer: b. Mean performance difference between modelspowerdrill+1
What does the p-value in a t-test indicate?
a. Probability of correctness of AI model
b. Strength of the alternative hypothesis
c. Likelihood the observed difference is due to chance
d. Validation accuracy
Answer: c. Likelihood the observed difference is due to chancepowerdrill+1
In hypothesis testing using t-tests, what does the null hypothesis usually state?
a. Means are equal
b. Means are different
c. Variance is 0
d. Data is skewed
Answer: a. Means are equalmachinelearningplus+2
Which type of t-test compares the means of two independent samples?
a. One-sample t-test
b. Dependent t-test
c. Independent t-test
d. Paired t-test
Answer: c. Independent t-testscribbr+1
When is a paired t-test used in AI?
a. Comparing outputs of identical models on different datasets
b. Comparing predictions before and after model training
c. Evaluating predictions from two related groups
d. Comparing models from unrelated projects
Answer: c. Evaluating predictions from two related groupsmachinelearningplus+1
What does a significant t-test result suggest in the context of AI model performance?
a. Overfitting exists
b. No meaningful difference
c. Statistically significant difference between means
d. Both models have identical accuracy
Answer: c. Statistically significant difference between meansquadratichq+1
Which assumption is critical for an independent two-sample t-test?
a. Samples are dependent
b. Unequal variances
c. Samples are independent
d. Means are identical
Answer: c. Samples are independentgeeksforgeeks+1
What is the role of degrees of freedom in a t-test?
a. Determines input data quality
b. Influences the critical t value
c. Identifies overfitting
d. Detects outliers
Answer: b. Influences the critical t valuesavemyexams+1
In which AI scenario is a one-sample t-test most suitable?
a. Comparing two models
b. Comparing sample mean against a known value
c. Comparing two sample variances
d. Testing data normality
Answer: b. Comparing sample mean against a known valuejulius+1
Which of the following is NOT an assumption of t-test?
a. Homogeneity of variance
b. Independence
c. Normal distribution
d. Large sample size required
Answer: d. Large sample size requiredgeeksforgeeks+1
What does a low p-value (typically < 0.05) indicate in a t-test?
a. Accept the null hypothesis
b. Reject the null hypothesis
c. Data is not normal
d. Increase model complexity
Answer: b. Reject the null hypothesispowerdrill+1
T-test can be used in AI experiments to:
a. Check feature importance
b. Compare means of model errors across two datasets
c. Visualize clustering
d. Reduce dataset size
Answer: b. Compare means of model errors across two datasetsmachinelearningplus+1
What is the standard significance level (alpha) used in t-tests?
a. 0.25
b. 0.10
c. 0.05
d. 0.90
Answer: c. 0.05quadratichq+1
What happens if the t-value exceeds the critical value in a t-test?
a. Accept the alternative hypothesis
b. Accept the null hypothesis
c. Increase sample size
d. Rerun test with different data
Answer: a. Accept the alternative hypothesisjulius+1
In machine learning, a t-test can help determine:
a. Whether feature scaling is needed
b. If performance difference between two algorithms is statistically significant
c. If datasets are related
d. The optimal number of clusters
Answer: b. If performance difference between two algorithms is statistically significantmachinelearningplus+1
When comparing model A and model B on the same test data, which t-test applies?
a. Independent t-test
b. One-sample t-test
c. Paired t-test
d. ANOVA
Answer: c. Paired t-testjulius+1
What does a very high p-value (> 0.05) typically suggest in a t-test?
a. Strong evidence against null hypothesis
b. Fail to reject the null hypothesis
c. Data is too biased
d. Means are very different
Answer: b. Fail to reject the null hypothesispowerdrill+1
Homogeneity of variance in t-tests refers to:
a. Data being normalized
b. Variance consistency between groups
c. Dataset size being equal
d. Model performance equality
Answer: b. Variance consistency between groupsgeeksforgeeks+1
The formula for the t-value in a two-sample t-test uses:
a. Mean and variance of one sample
b. Pooled mean and variance of two samples
c. Only standard deviation of first sample
d. Median values
Answer: b. Pooled mean and variance of two samplesscribbr+1
When using t-tests in neural network evaluation, the aim may be:
a. Improve convergence speed
b. Compare mean errors across models
c. Classify new inputs
d. Tune hyperparameters
Answer: b. Compare mean errors across modelsmachinelearningplus+1
Which test would you choose to compare the before and after accuracy of the same AI model using the same test set?
a. Welch’s t-test
b. Independent t-test
c. Paired t-test
d. Regression test
Answer: c. Paired t-testjulius+1
The outcome of a t-test largely depends on:
a. Dataset name
b. Sample size and variance
c. Feature selection method
d. Model type
Answer: b. Sample size and variancequadratichq+1
Adjusting the alpha level in a t-test affects:
a. Probability of type I error
b. Test dataset size
c. Performance metric calculation
d. Data normalization process
Answer: a. Probability of type I errorpowerdrill+1
Which t-test is appropriate for comparing two groups with unequal variances?
a. One-sample t-test
b. Standard independent t-test
c. Welch’s t-test
d. ANOVA
Answer: c. Welch’s t-testmachinelearningplus+1
AI practitioners use t-tests to:
a. Visualize data distribution
b. Perform statistical inference
c. Apply regularization
d. Generate synthetic data
Answer: b. Perform statistical inferencegeeksforgeeks+2
What is the consequence of violating the normality assumption in t-tests?
a. Increased statistical power
b. Increased risk of false results
c. Better clustering
d. Enhanced correlation
Answer: b. Increased risk of false resultsgeeksforgeeks+1
The null hypothesis in most t-tests assumes:
a. Group means are equal
b. Variances are different
c. Groups are dependent
d. Data is categorical
Answer: a. Group means are equalwikipedia+1
An AI researcher uses t-tests to:
a. Analyze image features
b. Determine if algorithm improvement is statistically significant
c. Train reinforcement agents
d. Optimize batch size
Answer: b. Determine if algorithm improvement is statistically significantpowerdrill+1
In t-tests, what is “degrees of freedom” associated with?
a. Number of AI models
b. Sample size minus constraints
c. Number of features
d. Value of p
Answer: b. Sample size minus constraintssavemyexams+1
Homogeneity of variance can be checked by:
a. Performing Levene’s test
b. Increasing alpha level
c. Decreasing feature size
d. Tuning hyperparameters
Answer: a. Performing Levene’s testjulius+1
To perform a paired t-test in AI, the data should be:
a. Clustered by similarity
b. Matched or related pairs
c. From different domains
d. Centered and scaled
Answer: b. Matched or related pairsmachinelearningplus+1
Which situation requires an independent t-test?
a. Pre-training and post-training results of the same AI model
b. Datasets from two unrelated experiences
c. Paired measurements
d. Time series data comparison
Answer: b. Datasets from two unrelated experiencesscribbr+1
Which test best suits comparing three or more group means?
a. T-test
b. F-test (ANOVA)
c. Z-test
d. Chi-square test
Answer: b. F-test (ANOVA)scribbr+1
What does ‘statistically significant’ mean in the context of a t-test in AI?
a. Difference is meaningful to users
b. Difference is unlikely due to random chance
c. Both models must have high accuracy
d. Data is normalized
Answer: b. Difference is unlikely due to random chancepowerdrill+1
The outcome variable in a t-test should be:
a. Categorical
b. Continuous
c. Ordinal
d. Binary
Answer: b. Continuousscribbr+1
If the p-value is exactly equal to the alpha level, the decision is to:
a. Reject the null hypothesis
b. Fail to reject the null hypothesis
c. Repeat the test
d. Increase model complexity
Answer: b. Fail to reject the null hypothesisjulius+1
One important use of t-tests in AI is:
a. Sorting datasets
b. Testing statistical significance of model changes
c. Training unsupervised models
d. Hyperparameter optimization
Answer: b. Testing statistical significance of model changesmachinelearningplus+1
When is it appropriate to use Welch’s t-test in AI?
a. When variances are unequal
b. For time series data
c. When data is categorical
d. When alpha is low
Answer: a. When variances are unequaljulius+1
Which scenario would violate the independence assumption of a t-test?
a. Two models trained on overlapping datasets
b. Two groups with the same sample size
c. Datasets from different sources
d. Models trained with different optimizers
Answer: a. Two models trained on overlapping datasetsgeeksforgeeks+1
In a one-sample t-test, the test compares:
a. Two independent means
b. A sample mean to a hypothesized value
c. The variances of two groups
d. Medians of two groups
Answer: b. A sample mean to a hypothesized valuescribbr+1
A major application of t-tests in supervised AI tasks is:
a. Cross-validation
b. Measuring variance between models
c. Comparing model performance with baseline
d. Feature filtering
Answer: c. Comparing model performance with baselinepowerdrill+1
The t-distribution is used instead of the normal distribution in t-tests when:
a. The population variance is unknown
b. The sample size is large
c. Data is categorical
d. Data is normalized
Answer: a. The population variance is unknownwikipedia+2
What can cause a t-test to become invalid in AI experiments?
a. Random splitting of data
b. Strong violation of normality/independence
c. Different AI algorithms
d. Increasing model epochs
Answer: b. Strong violation of normality/independencegeeksforgeeks+1
In a two-tailed t-test, significance means:
a. The mean is either significantly higher or lower
b. Only higher values matter
c. Only lower values matter
d. Direction of difference is ignored
Answer: a. The mean is either significantly higher or lowermachinelearningplus+1
The main difference between a t-test and a z-test lies in:
a. Value of p
b. Use of population vs sample variance
c. Data type
d. Number of variables
Answer: b. Use of population vs sample variancesavemyexams+1
T-tests help AI researchers ensure that observed model improvements are:
a. Due to random variation
b. Statistically reliable and significant
c. Only visual representations
d. Related to model architecture
Answer: b. Statistically reliable and significantpowerdrill+1
If sample sizes are very small, t-test is preferred over:
a. ANOVA
b. Z-test
c. Regression test
d. F-test
Answer: b. Z-testwikipedia+2
In the context of AI, t-test can be used to compare:
a. Model hyperparameters directly
b. Mean errors of models on different datasets
c. Training epochs
d. Class labels
Answer: b. Mean errors of models on different datasetsmachinelearningplus+1

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