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Mathematics ⚡ Grade 6 Data Spread: Center, Range, IQR, and MAD
⚡ Grade 6 · Lesson 13 of 14

Data Spread: Center, Range, IQR, and MAD

Drag data points and watch how an outlier pulls the mean but barely nudges the median.

Grade 6Middle School
Data Spread: Center, Range, IQR, and MAD — illustration
💡
The big idea: A statistical question expects variability in its answers. Once you collect that data, a single number is never the whole story — you need a measure of center (a typical value) and a measure of spread (how much the values vary) to describe the distribution honestly.
🎯 By the end, you'll be able to
  • Recognize a statistical question as one that anticipates variability in the data
  • Describe a distribution by its center, spread, and overall shape
  • Compare mean and median as measures of center, including how outliers affect each
  • Calculate range, interquartile range (IQR), and mean absolute deviation (MAD) as measures of spread
  • Choose an appropriate display — dot plot, histogram, or box plot — based on a distribution's shape
📎 You should already know
  • Mean and average (Grade 5)
  • Ordering and comparing whole numbers
  • Basic operations with decimals

What makes a question "statistical"?

Not every question is a statistical question. 'How old is the school principal?' has one fixed answer — there's no variability, so it isn't statistical. But 'How many minutes did each 6th grader spend on homework last night?' anticipates variability: different students will report different numbers.

A statistical question is one where you expect the answers to vary — and that variability is exactly what you're trying to describe and measure.

Describing a distribution: center, spread, shape

Once you collect the answers to a statistical question, you have a distribution — the natural shape of a set of data. Every distribution can be described by three things:

  • Center — a typical or representative value (mean or median).
  • Spread — how much the values vary (range, IQR, or MAD).
  • Shape — roughly symmetric, skewed to one side, or with outliers?

Reporting just one number ('the average is 12') is never the whole story. Spread tells you how much to trust that single number as 'typical.'

🔑 Mean vs. median — how outliers treat them differently

Mean — add every value and divide by the count. It uses every data point, but a single extreme value can pull it a long way.

Median — the middle value once the data is ordered (average the two middle values if the count is even). It only depends on position, so an outlier barely moves it.

Rule of thumb: in a roughly symmetric distribution with no big outliers, mean and median land close together. When the data is skewed or has an outlier, they pull apart — and the median is usually the more honest 'typical' value.

Three ways to measure spread

MeasureHow to compute itSensitivity to outliers
Rangemax − minVery high — uses only the 2 extreme points
IQRQ3 − Q1 (spread of the middle 50%)Low — ignores the extremes entirely
MADaverage of |value − mean|Moderate — uses every point, but less swayed than the mean/range

To find IQR: order the data, locate the median (Q2), then find the median of the lower half (Q1) and the median of the upper half (Q3). IQR = Q3 − Q1.

To find MAD: compute the mean, then find how far each value sits from that mean (dropping the sign), and average those distances.

🎮 Bean Plant Heights — Drag the Dots LIVE
Nine bean seedlings measured after three weeks (in cm), with one plant that grew unusually tall near a sunny window. Drag the tall dot toward the others and watch the mean chase it while the median barely moves. Add, remove, or randomize points to build your own data set.
✨ Pair the right center with the right spread

Mean and MAD travel together — both use every value in their calculation, so they work best for roughly symmetric data. Median and IQR travel together — both are based on position in the ordered list, so they hold up when data is skewed or has outliers.

The display matters too (6.SP.4): a dot plot is great for small data sets, a histogram groups values into bars for larger sets, and a box plot shows the five-number summary (min, Q1, median, Q3, max) at a glance — perfect for comparing spread and skew quickly.

⚠️ Range only looks at two points

Range is easy to compute, but it depends entirely on the smallest and largest values — one unusual measurement can make a tight, consistent data set look wildly spread out. A class where everyone scored 80–90 except for one 20 has a huge range (70), even though nearly every student's score was close together.

Whenever an outlier is present, report IQR or MAD alongside (or instead of) range — they describe the spread of the typical data, not just its extremes.

📝 Worked example: Six friends recorded how many push-ups they could do: 8, 10, 10, 11, 12, 40 (one is a competitive athlete). Find the mean and median, and explain why they differ so much.
  1. Mean: sum = 8+10+10+11+12+40 = 91; 91 ÷ 6 ≈ 15.2.
  2. Median: the data is already ordered. With 6 values, the median is the average of the 3rd and 4th: (10+11)/2 = 10.5.
  3. The mean (≈15.2) is pulled upward by the outlier of 40, while the median (10.5) stays close to where most of the data actually sits.
✓ Mean ≈ <strong>15.2</strong>; median = <strong>10.5</strong>. The median better represents a 'typical' friend here.
📝 Worked example: Find the range and the IQR of the ordered data set: 3, 5, 6, 8, 9, 11, 14, 16, 20 (9 values).
  1. Range = max − min = 20 − 3 = 17.
  2. The median (Q2) is the 5th value: 9.
  3. Lower half (3, 5, 6, 8) → Q1 = (5+6)/2 = 5.5.
  4. Upper half (11, 14, 16, 20) → Q3 = (14+16)/2 = 15.
  5. IQR = Q3 − Q1 = 15 − 5.5 = 9.5.
✓ Range = <strong>17</strong>; IQR = <strong>9.5</strong>.
📝 Worked example: Find the mean absolute deviation (MAD) of the data set 4, 6, 8, 10, 12.
  1. Mean = (4+6+8+10+12) ÷ 5 = 40 ÷ 5 = 8.
  2. Absolute deviations from the mean: |4−8|=4, |6−8|=2, |8−8|=0, |10−8|=2, |12−8|=4.
  3. Average the deviations: (4+2+0+2+4) ÷ 5 = 12 ÷ 5 = 2.4.
✓ MAD = <strong>2.4</strong> — on average, each data point sits 2.4 units away from the mean of 8.

Check your understanding

1. Which question is a statistical question?
A statistical question expects variability in the answers. Asking every student how many pets they have will produce different answers; the others have one fixed answer.
2. A data set of 9 plant heights has one unusually tall outlier plant. Which measure of center is least affected by that outlier?
The median only depends on the middle value's position once the data is ordered, so a single very large or small outlier barely changes it. The mean is pulled toward the outlier because it uses the sum of every value.
3. A data set is 4, 7, 7, 9, 12, 15, 20. What is the range?
Range = max − min = 20 − 4 = 16.
4. For the ordered data 2, 4, 5, 7, 9, 12, 15, what is the interquartile range (IQR)?
Median = 7. Lower half (2, 4, 5) has median Q1 = 4. Upper half (9, 12, 15) has median Q3 = 12. IQR = Q3 − Q1 = 12 − 4 = 8.
5. A distribution is heavily skewed with a few very large outliers. Which pairing of statistics best describes it?
When data is skewed or has outliers, the median and IQR are more resistant to those extreme values than the mean and MAD, giving a more accurate picture of the typical data and its spread.
✅ Key takeaways
  • A statistical question anticipates variability in the answers — 'how tall is Maria' isn't one, but 'how tall are the students in 6th grade' is.
  • A distribution is described by its center, its spread, and its overall shape.
  • Mean and median both measure center, but the median is far more resistant to outliers.
  • Range (max − min), IQR (Q3 − Q1), and MAD (average distance from the mean) all measure spread — IQR and MAD are more informative than range alone.
  • Pair mean with MAD for roughly symmetric data; pair median with IQR when the data is skewed or has outliers.