Reflection and preview of next lesson
You've just learned the foundational tools that professional data analysts use every day. Mean, median, and range aren't just abstract math—they're the tools that let Sarah answer "What's a typical weekend?" with real numbers instead of guesses.
What You Can Now Do:
- • Calculate mean: Add all values, divide by count
- • Find median: Sort data, find middle value
- • Compute range: Maximum minus minimum
- • Choose the right measure: Mean for symmetric data, median when outliers are present
Statistical Thinking
- Statistics summarize large datasets into understandable numbers
- Different measures tell different stories—choose the one that fits your data
- Mean ≈ median suggests symmetric data; large gap suggests outliers
Business Application
- "Typical" means different things depending on data shape
- Outliers can drastically change the mean but not the median
- Range tells you about consistency, not center
In the next lesson, we'll tackle a problem we saw in this lesson's data: What do we do with outliers?
Lesson 03 Preview:
- • How do we identify what counts as an "outlier"?
- • Should outliers be removed, adjusted, or kept?
- • What does an outlier tell us about the business?
- • How do we make defensible data-quality decisions?
Remember that $2,100 weekend from our data? That outlier is trying to tell us something. In the next lesson, we'll learn how to listen.
You started this unit by learning about the café's problem: they're throwing away too much inventory on weekends. Now you have your first tool for solving it.
How Statistics Helps:
- • Know what's "typical" so you can plan the right amount of inventory
- • Understand how consistent (or variable) weekend sales are
- • Identify which weeks are truly special vs. normal variation
- • Make inventory decisions based on evidence, not guesses
Next lesson, we'll learn what to do when the data includes values that seem "wrong"— and why those wrong values might actually contain important business insights.