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Research & Data Analysis Flashcards

7 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Research & Data Analysis flashcards as text
  1. A Likert-scale survey question (1–5) measuring satisfaction is best analyzed with which statistical approach?

    Answer: Mann-Whitney U test as a non-parametric alternative

    Ordinal Likert data often violates normality assumptions, making the Mann-Whitney U test a robust non-parametric choice for group comparisons.

  2. In a multiple regression model, the variance inflation factor (VIF) is used to detect:

    Answer: Multicollinearity among predictors

    VIF quantifies how much a predictor's variance is inflated due to linear correlations with other predictors in the model.

  3. Which sampling method ensures proportional representation of subgroups within the population?

    Answer: Stratified random sampling

    Stratified random sampling divides the population into strata and samples from each proportionally, guaranteeing subgroup representation.

  4. A receiver operating characteristic (ROC) curve plots sensitivity against:

    Answer: 1 – Specificity (false positive rate)

    The ROC curve plots the true positive rate (sensitivity) on the y-axis against the false positive rate (1 – specificity) on the x-axis across all classification thresholds.

  5. In Bayesian inference, the posterior distribution is proportional to:

    Answer: The product of the prior and the likelihood

    Bayes' theorem states posterior ∝ likelihood × prior; the marginal likelihood acts as a normalizing constant.

  6. A researcher finds r = 0.92 between two variables but the relationship is explained entirely by a third variable. This is called:

    Answer: Spurious correlation

    A spurious correlation appears strong but disappears when a confounding variable is controlled, revealing no true direct relationship.

  7. When building a predictive model, overfitting is best identified by:

    Answer: High training accuracy and low test accuracy

    Overfitting occurs when a model memorizes training data, yielding high in-sample performance but poor generalization to unseen test data.

Research & Data Analysis Flashcards — MS-DS Master of Data science Study Cards with Answers