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
A data science team applies natural language processing to classify customer reviews. To evaluate model fairness across demographic groups, which metric should they prioritize?
Answer: Equalized odds across groups
Equalized odds requires equal true positive and false positive rates across demographic groups, directly measuring fairness beyond aggregate performance.
In experimental design, a Latin square design is used to control for:
Answer: Two nuisance variables simultaneously
A Latin square balances two blocking factors (rows and columns) alongside the treatment, controlling both sources of nuisance variability.
Which of the following best describes the curse of dimensionality in data analysis?
Answer: As features increase, the data becomes sparse and distance metrics lose meaning
In high dimensions, data points become equidistant and sparse, degrading the performance of distance-based algorithms and increasing sample size requirements.
An analyst uses a Bonferroni correction after running 20 simultaneous hypothesis tests at α = 0.05. The corrected per-test significance level is:
Answer: 0.0025
Bonferroni correction divides alpha by the number of tests: 0.05 / 20 = 0.0025, controlling the familywise error rate.
In survival analysis, censored observations occur when:
Answer: A subject leaves the study or the study ends before the event is observed
Censoring means the exact event time is unknown because the subject was lost to follow-up or the study concluded before the event occurred.
A data analyst notices that residuals from a regression model show a funnel-shaped pattern when plotted against fitted values. This indicates:
Answer: Heteroscedasticity
A funnel-shaped residual plot indicates heteroscedasticity: the variance of residuals changes as a function of fitted values, violating OLS assumptions.
When using information criteria to compare competing statistical models, AIC penalizes model complexity by:
Answer: Adding twice the number of estimated parameters to the negative log-likelihood
AIC = 2k – 2ln(L), where k is the number of parameters; the 2k term penalizes complexity to guard against overfitting.