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 researcher collects data from volunteers who self-select into a study. The primary threat to external validity is:
Answer: Self-selection bias
Self-selection bias occurs when participants who volunteer differ systematically from the target population, limiting generalizability.
When comparing more than two group means simultaneously, a one-way ANOVA is preferred over multiple t-tests because:
Answer: Multiple t-tests inflate the Type I error rate
Running multiple t-tests increases the familywise error rate; ANOVA controls it by testing all groups in a single analysis.
A confusion matrix for a binary classifier shows TP=80, FP=10, FN=20, TN=90. What is the precision?
Answer: 0.89
Precision = TP / (TP + FP) = 80 / (80 + 10) = 80/90 ≈ 0.889.
In principal component analysis (PCA), the first principal component is defined as:
Answer: The linear combination of variables that explains the most variance
PC1 is the direction (linear combination) in the feature space along which the projected data has maximum variance.
A dataset contains 5% missing values that are Missing Not at Random (MNAR). The best imputation strategy is:
Answer: Multiple imputation with auxiliary variables explaining missingness
MNAR requires modeling the missingness mechanism; multiple imputation with variables that predict missing data reduces bias compared to simpler methods.
Effect size measures like Cohen's d are reported alongside p-values because:
Answer: They quantify the practical magnitude of a difference, which p-values do not
A statistically significant result can have a trivially small effect; Cohen's d communicates how large the difference actually is in standardized units.
In time series analysis, differencing a series is used primarily to achieve:
Answer: Stationarity
Differencing removes trends and seasonality, transforming a non-stationary series into a stationary one required for ARIMA modeling.