What 10 Cups of Coffee Can Teach You About Data

type

Post

status

Published

date

Mar 28, 2026 10:49

slug

ten-cups-of-coffee-descriptive-statistics

summary

Ten cups of coffee walk into a room — and suddenly you've got a masterclass in descriptive statistics.

What 10 Cups of Coffee Can Teach You About Data

Imagine ten people walk into a café. Each orders a coffee. Now imagine a data analyst walks in behind them. Suddenly those ten cups aren't just beverages — they're a dataset. And depending on what question you ask, you'll get ten different answers from exactly the same ten cups.

This is the quiet superpower of descriptive statistics: the data doesn't change, but your perspective on it does.

The Ten Questions You Could Ask

The Basics

1. Average caffeine per cup across the whole group Add up the caffeine in all ten cups and divide by ten. That gives you the mean caffeine per cup. It’s useful for estimating the overall level of caffeine in the group, but it can hide big differences between individuals — a triple-shot espresso and a decaf both get folded into the same average.

2. Average caffeine per person This only differs from the first question if people had different numbers of coffees. If each person has one cup, then average caffeine per person is the same as average caffeine per cup. If some people had two coffees and others had one, then the distinction matters — one is a property of the drinks, the other is a property of the drinkers. One is a product metric, the other is a consumption metric.

3. Average millilitres of milk per cup Now we're looking at a specific variable — milk. This is still a mean, but now we're looking at one ingredient rather than the drink as a whole. Note: if three people had no milk at all, do you include their zeros in the average? You should — but many people don't, which produces "average ml of milk from milk-based coffees only." That's a different question with a different answer.

4. Average cup size A classic mean calculation. But here's the thing: is a 480ml Venti Starbucks monster really "just a bit above average" compared to an 80ml espresso? Averages flatten variation. This is where the next two measures earn their keep.

5. Min/Max (Range) The smallest cup to the largest. Range tells you how spread out your data is — and sometimes that spread is the most interesting finding. A room where the smallest coffee is 80ml and the largest is 600ml tells a very different story than a room where every cup is between 200ml and 250ml.

The Patterns

6. Most frequently ordered type (Mode) If six out of ten people ordered a flat white, the flat white is your mode — the most common value in a categorical dataset. Mode is often overlooked, but it's the most useful measure when you're making decisions like "what should we put on special today?" Mean and median can't answer that for nominal categories like coffee type. Mode can.

7. Most common size AND type (Joint frequency pattern) Now you're cross-referencing two variables: size and type. This is where simple stats tip into pattern analysis — you're not just counting, you’re asking which combination occurred most often. "Most people ordered a medium flat white" is a richer insight than either the size or type alone.

8. Count with and without milk Binary categorisation — yes/no, milk/no milk. Simple count data. This might seem trivial, but in a real dataset it becomes the basis for segmentation: milk drinkers vs. black coffee drinkers might have completely different preferences on everything else too.

9. Count of venues purchased from Interesting shift here. Now you're not analysing the coffee — you're analysing the source. If all ten cups came from one café, that's a very different finding than if they came from ten different places. This is contextual information — data about the circumstances of the data, not the data itself. Easily overlooked, often crucial in how you interpret the results.

The Point

10. Is one of these yours? And here's where it gets personal. All the statistics in the world don't tell you which cup to pick up. Statistics can describe the group, summarise patterns, and support judgment to inform your decisions, but they can’t make the decision for you. The moment you ask "which one is mine?" you've moved from analysis to action, and that's a step only a human can take.

Why This Matters Beyond Coffee

Every one of those ten questions used a different lens on identical underlying data. This is exactly why data literacy matters — not just knowing how to calculate a mean, but knowing which measure to reach for depending on what decision you're trying to make.

Get the question wrong, and the right answer is useless. Ask the right question, and even ten cups of coffee can tell you something worth knowing.

Want to go deeper? Check out my review of Everydata — a whole book built on this exact idea.