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Statistical Analysis and Interpretation Questions

Demonstrates sound statistical methods for analyzing quantitative data and translating findings into actionable business insight. Core skills include descriptive statistics and distributional analysis (percentiles, median, mean, standard deviation, measures of dispersion), outlier detection and handling, correlation assessment, and basic to multivariable regression to control for confounding variables. Also covers hypothesis testing with interpretation of p-values and confidence intervals, evaluation of sample size and statistical power, and awareness of pitfalls such as confounding, multiple comparisons, and violations of model assumptions. Candidates should be able to choose appropriate statistical tests for a given analysis, validate models, and present statistical results clearly to nontechnical stakeholders.

EasyTechnical
15 practiced
You tell a stakeholder that the 95% confidence interval for the lift from a new recommendation algorithm is [1%, 9%]. They ask if that means there's a 95% chance the true lift is between 1% and 9%. How do you respond?
MediumTechnical
16 practiced
You've built a multivariable regression model to predict customer lifetime value from several features. Beyond looking at R-squared, how do you validate that the model is actually sound?
EasyTechnical
21 practiced
Say you're looking at customer order values for an e-commerce client and most orders are under $50 but a handful of enterprise orders run into the tens of thousands. Would you report the mean or the median order value to leadership, and why?
HardTechnical
20 practiced
In a multivariable regression predicting house prices, you notice that two of your predictors, square footage and number of rooms, are strongly correlated with each other, and their coefficients swing wildly (even flipping sign) when you add or remove other variables. What's going on and how would you handle it?
HardTechnical
22 practiced
You have a small sample, 15 customers, of time-to-resolution for support tickets in a new pilot process, and the data is heavily right-skewed with a couple of very long outlier resolutions. You want to compare it against the old process's typical resolution time. Why might a standard t-test be a bad choice here, and what would you use instead?

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