Phase 3: Data & Statistical Literacy · ~35 minutes · Python · uv
Descriptive Statistics — Center and Spread
Add one billionaire to a room of teachers and the mean income says everyone's suddenly rich. The median barely notices. That's not a coincidence.
Hiring signal: Reaches for the median instead of the mean when a dataset might have outliers, and can explain why in one sentence
What you will learn
- Compute mean, median, and mode, and explain when each one misleads
- Explain variance and standard deviation as a measure of how spread out data is
- Diagnose a case where the mean looks fine until the raw data reveals an outlier driving it
- Predict which of two same-mean datasets has more 'surprising' individual values, using spread
Introduction
Type: Learn Languages: Python Prerequisites: Lesson 01 (What Data Actually Is) Time: ~35 minutes
Objective
Learning objectives
- Compute mean, median, and mode, and explain when each one misleads
- Explain variance and standard deviation as a measure of how spread out data is
- Diagnose a case where the mean looks fine until the raw data reveals an outlier driving it
- Predict which of two same-mean datasets has more "surprising" individual values, using spread
What you're building
A script (spread_analysis.py) that:
- Computes mean, median, and mode on a real dataset of at least 8 values that you construct with one genuine, obvious outlier (not copied from this lesson)
- Prints how far apart the mean and median end up, and explains in a comment which one better represents "typical" for your specific dataset and why
- Constructs two different datasets with the same mean but visibly different spread (like
a and b above), computes stdev for both, and prints a one-sentence interpretation of what the spread difference means practically
Dataset X has mean 200 and stdev 5. Dataset Y has mean 200 and stdev 80. A single new value of 250 shows up in each. In which dataset is 250 more surprising?
This is the entire practical purpose of standard deviation: it defines what counts as "normal variation" for a specific dataset. In X, values barely stray from 200 (spread of just 5), so a value 50 away is extremely unusual — many standard deviations out. In Y, values routinely swing by around 80, so landing 50 away from the mean is unremarkable, well within Y's normal spread. The same raw distance from the mean means something completely different depending on how spread out the data typically is — which is exactly why "how far from average" alone, without knowing the spread, doesn't tell you whether something is actually unusual.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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