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.

MediumTechnical

69 practiced

How do you choose between a histogram, a boxplot, a violin plot, and a KDE/density plot when exploring a numeric column's distribution? What does each one emphasize about a distribution's shape, and which would you pick to present to a technical audience versus a non-technical one?

Sample Answer

EasyTechnical

62 practiced

How would you quantify and visualize class imbalance in a target variable during EDA, and at what point does an imbalance become a problem worth flagging to the modeling team?

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.

MediumTechnical

75 practiced

Beyond basic distribution plots, which visualizations are most useful early in exploration specifically for spotting anomalies, duplicates, and irregular time patterns -- think calendar heatmaps, run charts, and missingness heatmaps? What does each reveal that a summary statistic wouldn't?

Sample Answer

MediumTechnical

62 practiced

What's the difference between exploratory and confirmatory (inferential) analysis, and why does that distinction matter for how you report an EDA finding? Give an example where EDA changed the direction of a decision, and an example where you needed a formal statistical test before acting.

Sample Answer

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Chart	Best for	Main weakness
Histogram	full shape, gaps, modality	changes with bin width
Boxplot	comparing many groups compactly	hides bimodality
Violin plot	recovering shape a boxplot hides	needs more data to look stable
KDE	a smooth single-distribution view	bandwidth can under/overstate sharpness

Exploratory Data Analysis and Data Quality Questions

Sample Answer

Direct answer

What each emphasizes

Worked example

Trade-offs and pitfalls

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Direct answer

Why the threshold is fuzzy, not fixed

Worked example

Trade-offs and pitfalls

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Direct answer

Why this specifically biases the estimate

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

What each reveals

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

The transition from one to the other

Worked example

Trade-offs and pitfalls

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