Lab 1A — Univariate Explorer

Objective: Understand how distribution shape, outliers, and summary statistics relate visually. Explore salary data using interactive histograms and boxplots, then inject outliers to see which statistics are robust.

📊 Histogram / Density 0 outliers injected

Bins: 20

📦 Boxplot

Mean

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Arithmetic average — sensitive to outliers

Median

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Middle value — robust to outliers

Std Dev (σ)

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Spread around mean

IQR

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Q3 − Q1 — robust spread measure

🎯 Challenge

"Inject 3 outliers. Which statistic changes more — mean or median? Why does this matter for salary reporting? What happens to the IQR?"

Self-Check Rubric

Did the mean shift more than the median after injecting outliers? Did the IQR remain relatively stable even with outliers present? Can you explain why median is preferred for salary reporting? Did you try at least 3 different bin counts and observe the distribution shape change?

📝 Reflection (auto-saved):

💉 Predict Before You Inject

An outlier of 3–6× the median is about to enter the dataset. Make your predictions first — then see if you were right.

1. Will the mean go:

2. Will the median move:

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