Activate prior knowledge and surface the need for statistics
In the last lesson, you helped Sarah see that the campus café faces a real problem: they're throwing away 8-12% of weekend inventory while trying to serve students fairly.
Now the manager has given Sarah a full month of weekend sales data—48 Saturdays and Sundays worth of transactions. She can see that sales vary wildly from weekend to weekend. But she needs to answer one simple question: What's a "normal" weekend?
The Friction Point
Sarah can't just look at the numbers and know what's typical. She has 48 data points, and they don't all line up neatly. She needs descriptive statistics—a mathematical way to summarize what "normal" actually looks like.
Here are last month's weekend sales totals (in dollars):
What do you notice?
- • Some values are close together
- • One value is much higher
- • Numbers range from low 400s to over 2,000
What do you wonder?
- • What's a "typical" weekend?
- • Why is one weekend so much higher?
- • How can we summarize all 48 numbers?
Descriptive statistics are the tools that let us take a pile of numbers and turn them into something we can actually understand and use for decisions.
The arithmetic average—sum all values, divide by count. Tells us the "balancing point" of the data.
The middle value when sorted—half above, half below. Resistant to outliers that distort the mean.
How spread out the values are. A narrow spread means values cluster near the center.