Comparing Distributions — I can compare two distributions...

SECTION 1

Why Do We Compare Data?

People have been collecting and comparing data for hundreds of years. Farmers compared harvests from different fields. Doctors compared the health of patients who took different medicines. Whenever you want to know which option is better, you need a way to compare distributions (groups of data values). Over time, mathematicians developed tools that make these comparisons fair and clear.

1663

John Graunt's Life Tables

John Graunt studied birth and death records in London. He compared data across different years. This was one of the first uses of statistics to understand groups of numbers.

1800s

The Rise of Averages

Scientists began using the mean (average) and median to summarize data sets. This made it much easier to compare two groups with a single number.

1900s

Measuring Spread

Statisticians realized that just knowing the center wasn't enough. They developed measures of spread, like range and mean absolute deviation (MAD), to describe how spread out data is.

Today

Data Everywhere

We compare distributions every day — test scores between classes, ratings of apps, sports stats across teams. Understanding center and spread helps us make smart decisions.

So here is the big question this lesson answers: when you have two groups of data, how do you decide which group did better, or how the groups are different? You need to look at the center and the spread — and then explain what those numbers mean in context.

SECTION 2

Core Ideas: Center, Spread, and Context

Before you compare two data sets, you need to understand three big ideas. Let's break them down one at a time.

Center

The center tells you where the "middle" of the data is. Common measures are the mean (add all values and divide by how many) and the median (the middle value when data is in order).

Spread

The spread tells you how spread out or bunched together the data values are. Common measures are the range (max − min) and the mean absolute deviation (MAD).

Shape

The shape of a distribution shows whether data is symmetric, skewed left, or skewed right. Shape helps you decide whether to use the mean or median.

Contextual Conclusion

Numbers alone aren't enough. A contextual conclusion explains what the center and spread mean in real life. For example: "Class A scored higher on average and was more consistent."

✦ KEY TAKEAWAY

Think of center and spread like a basketball team. The center (mean or median) is like the team's typical score per game. The spread (range or MAD) is like how predictable that team is — do they always score about the same, or do they have wild ups and downs? To really compare two teams, you need both pieces of information.

SECTION 3

Seeing Two Distributions Side by Side

The best way to understand comparing distributions is to look at a picture. The dot plot below shows quiz scores for two classes. Each dot represents one student's score.

Both classes have a mean of 8, but Class B's scores are much more spread out (MAD = 2.5 vs. 1.2). The dashed lines mark the mean for each class.

Notice how both classes have the same mean (8), but they look very different! Class A's scores are clustered near 8, while Class B has scores scattered from 5 all the way to 10. This is exactly why you need to look at both center and spread when comparing two distributions. The center alone does not tell the whole story.

SECTION 4

The Math Behind Center and Spread

Let's look at the formulas you'll use. Don't worry — each one is just a recipe with clear steps.

MEAN (AVERAGE)

Mean = (sum of all values) ÷ (number of values)

Add every data value together, then divide by how many values you have. The mean tells you the balance point of the data.

MEDIAN

Median = middle value when data is listed from least to greatest

If there is an even number of values, the median is the mean of the two middle values. The median is useful when there are outliers (extreme values) because it is not pulled by them.

RANGE

Range = maximum value − minimum value

The range gives you a quick sense of how spread out the data is. A bigger range means the data values cover more ground.

MEAN ABSOLUTE DEVIATION (MAD)

MAD = (sum of |each value − mean|) ÷ (number of values)

The | | symbols mean "absolute value" — ignore any negative signs. MAD tells you the typical distance a data point sits from the mean. A smaller MAD means the data is more consistent.

💡 Which Measure Should I Use?

Use the mean and MAD when the data is roughly symmetric (balanced). Use the median and range (or IQR) when the data is skewed or has outliers.

SECTION 5

A Step-by-Step Comparison Framework

Every time you compare two distributions, follow these four steps. Think of it like a checklist. The diagram below shows the process.

Follow these four steps every time you compare two data sets: find the center, find the spread, compare them, and write a contextual conclusion.

The most important step is the last one — the contextual conclusion. A contextual conclusion uses the topic of the data, not just the numbers. Instead of saying "Group 1 has a higher mean," you might say, "Students who studied with flashcards scored about 12 points higher on average than students who only re-read their notes." That sentence tells the reader what was measured, who was being compared, and how much of a difference there was.

SECTION 6

Worked Example: Comparing Two Soccer Teams

The Eagles and the Falcons each played 6 soccer games. Here are the number of goals each team scored per game.

Goals scored per game for the Eagles and the Falcons

Game	Eagles	Falcons
1	2	0
2	3	1
3	2	5
4	4	1
5	3	6
6	4	5

Comparing the Eagles and the Falcons

Step 1 — Find the Mean of Each TeamEagles: (2 + 3 + 2 + 4 + 3 + 4) ÷ 6 = 18 ÷ 6 = 3. Falcons: (0 + 1 + 5 + 1 + 6 + 5) ÷ 6 = 18 ÷ 6 = 3. Both teams have the same mean.

Eagles mean = 3 goals, Falcons mean = 3 goals

Step 2 — Find the MAD of Each TeamEagles: Deviations from the mean (3): |2−3| + |3−3| + |2−3| + |4−3| + |3−3| + |4−3| = 1 + 0 + 1 + 1 + 0 + 1 = 4. MAD = 4 ÷ 6 ≈ 0.67. Falcons: Deviations from the mean (3): |0−3| + |1−3| + |5−3| + |1−3| + |6−3| + |5−3| = 3 + 2 + 2 + 2 + 3 + 2 = 14. MAD = 14 ÷ 6 ≈ 2.33.

Eagles MAD ≈ 0.67, Falcons MAD ≈ 2.33

Step 3 — Compare Center and SpreadThe means are equal (both 3). However, the Eagles have a much smaller MAD (0.67 vs. 2.33). This means the Eagles score about the same number of goals each game, while the Falcons' scores jump around a lot.

Same center, but the Eagles are far more consistent

Step 4 — Write a Contextual ConclusionPut it all together in a sentence that references soccer, not just numbers.

Both teams averaged 3 goals per game, but the Eagles were much more consistent (MAD ≈ 0.67) compared to the Falcons (MAD ≈ 2.33). The Falcons had some high-scoring games but also some shutouts, so their performance was less predictable.

SECTION 7

When to Use Which Measure

Not every measure of center and spread is perfect for every situation. The table below helps you choose.

Strengths and limitations of common measures

Measure	Strengths	Limitations
Mean	Uses every data value; good for symmetric data.	Pulled toward outliers (extreme values). Can be misleading if data is skewed.
Median	Not affected by outliers; great for skewed data.	Ignores the actual size of extreme values.
Range	Quick and easy to calculate.	Only uses two values (max and min). One outlier can make the range huge.
MAD	Uses every data value; tells you the typical distance from the mean.	Takes more time to calculate. Only paired with the mean, not the median.

✦ KEY TAKEAWAY

Imagine you're choosing a pizza restaurant. One place always delivers in about 30 minutes (low spread). Another averages 30 minutes but sometimes takes 15 and sometimes 50 (high spread). The average alone makes them look the same, but the spread tells you which one you can count on. Always report both!

SECTION 8

From MAD to Standard Deviation

In this lesson you've been using the MAD to measure spread. In high school and beyond, you'll learn a related measure called standard deviation. It does the same job — measuring how spread out data is — but uses squares instead of absolute values. The ideas you're building now will make that transition smooth.

MAD vs. Standard Deviation

Feature	MAD (this lesson)	Standard Deviation (future)
What it measures	Average distance from the mean	A kind of average squared distance from the mean
How it handles negatives	Absolute value (\| \|)	Squaring (²)
When you learn it	Middle school	High school / Algebra 2 and beyond
Core idea	Bigger value = more spread	Same! Bigger value = more spread

You'll also learn about box plots and the interquartile range (IQR), which give even more detail about how data is distributed. For now, just remember: the skills you're learning — comparing center and spread and writing a contextual conclusion — are the foundation for everything that comes next.

SECTION 9

Practice Problems

PROBLEM 1 — CONCEPTUAL

Two data sets have the same mean but different MAD values. What does a larger MAD tell you about a data set compared to a smaller MAD?

PROBLEM 2 — BASIC CALCULATION

Team X scored these points in 5 basketball games: 40, 45, 50, 55, 60. Find the mean and the MAD.

PROBLEM 3 — INTERMEDIATE

Group A test scores: 70, 75, 80, 85, 90. Group B test scores: 60, 70, 80, 90, 100. Find the mean and MAD for each group. Which group performed more consistently?

PROBLEM 4 — APPLIED

A farmer measures rainfall (in cm) at two fields over 6 months. Field 1: 5, 6, 5, 7, 6, 7. Field 2: 2, 3, 10, 9, 4, 8. Compare the distributions and write a contextual conclusion about which field has more reliable rainfall.

PROBLEM 5 — CRITICAL THINKING

A coach says, "Team A is better than Team B because Team A has a higher mean score." Is the coach's reasoning complete? What additional information should the coach consider, and why might that change the conclusion?

SUMMARY

Lesson Summary

To compare two distributions, you need two key measurements. The center (usually the mean or median) tells you where the typical value falls. The spread (measured by the range or MAD) tells you how consistent or variable the data is. Two groups can have the same center but look completely different when you check the spread.

Always finish with a contextual conclusion — a sentence that explains the comparison using the real-world topic of the data, not just numbers. Follow the 4-step framework: calculate center, calculate spread, compare them, and write your conclusion. This skill is the foundation for more advanced statistics you will learn in high school, including standard deviation and box plots.