Exploratory Data Analysis and Data Quality Questions

Understanding and preparing an unfamiliar dataset before analysis or modeling. Covers systematic profiling through summary statistics, distribution and outlier inspection, and relationships between variables to form initial hypotheses, alongside turning raw data into a trustworthy base: handling missing values, deduplication, outlier treatment, type and consistency checks, and validation. Includes critical thinking about sampling and measurement bias and what a dataset can and cannot support.

HardTechnical

59 practiced

A model's performance on held-out data is suspiciously high. During EDA, what checklist of checks would you run to confirm or rule out data leakage: comparing feature distributions across splits, looking for duplicate rows across train/val/test, verifying time-based splits, and running a simple model on a single suspicious feature?

Sample Answer

HardTechnical

74 practiced

For a heavy-tailed metric (think financial transaction sizes), what robust descriptive statistics would you reach for beyond mean/variance -- trimmed mean, winsorized mean, median absolute deviation -- and what does each protect you against that the standard versions don't?

Sample Answer

EasyTechnical

66 practiced

A dashboard scatter plot shows a negative correlation between average nightly price and occupancy rate across listings. What hypotheses would you generate to explain that pattern, what additional data would you request, and what analyses would you run to test those hypotheses?

Sample Answer

HardTechnical

68 practiced

A customer-lifetime-value analysis is skewed because older purchase histories are truncated (you only see what's left in the system). How would you quantify the bias this truncation introduces during EDA, and what would you flag as an assumption before anyone treats the resulting estimate as reliable?

Sample Answer

Direct answer Quantify truncation bias by comparing what you can see (the system's stored, truncated history) against any evidence of what's actually missing, and flag the resulting estimate as a likely UNDERSTATEMENT before anyone treats it as reliable. Older customers, whose early purchase history has fallen outside whatever retention window the system stores, will show a systematically lower observed lifetime value than their true value, purely because more of their history has aged out. Why this specifically biases the estimate If the system only retains, say, the last two years of purchase history, a customer with ten years of tenure has 80% of their true history invisible to any analysis running on the stored data, while a customer with one year of tenure has essentially all of their history visible. Averaging "observed lifetime value" across customers with very different tenures conflates two very different amounts of truncation, systematically understating true value more severely for your longest-tenured, and often most valuable, customers. Worked example Simulating this directly: assume a true, fully-observed average customer lifetime value of about $102, but a stored system that only retains roughly the last two years of history relative to each customer's total tenure. The resulting observed average, computed only from what's actually stored, comes out to about $51, roughly a 50% understatement of the true figure, driven almost entirely by the longest-tenured customers whose visible history represents the smallest fraction of their true lifetime. Trade-offs and pitfalls Before treating any truncation-corrected estimate as reliable, be explicit about the assumption behind the correction (in this case, an assumed relationship between tenure and how much history is missing) and flag it as an assumption, not a fact, since a wrong assumption about the truncation pattern can make a "corrected" number just as misleading as the uncorrected one, only with more false confidence attached.

EasyTechnical

75 practiced

Walk through the common data types you'll encounter in a new table (integer, float, string, datetime, boolean, categorical) and one pitfall each causes when mis-typed (for example, a datetime stored as a string). How would you detect and confirm a mis-typed column during your first pass?

Sample Answer

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Statistic	What it replaces	What happens to extreme values
Trimmed mean	the plain mean	dropped entirely before averaging
Winsorized mean	the plain mean	kept, but capped at a threshold
MAD	standard deviation / variance	barely influence it at all

Exploratory Data Analysis and Data Quality Questions

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Direct answer

Why each check catches a different flavor of leakage

Worked example

Trade-offs and pitfalls

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Direct answer

What each protects against, specifically

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Generating and narrowing hypotheses

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Why this specifically biases the estimate

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Detecting a mis-typed column

Worked example

Trade-offs and pitfalls

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