🎯 Why Study Measures of Dispersion?
Averages tell only half the story.
Two data sets may have the same mean but wildly different spread. That’s why understanding dispersion — how data varies — is essential for interpreting data accurately.
Whether you’re taking IB Math, A Level Statistics, or AP Stats, these tools will help you analyze data more meaningfully.
🧠 Learning Objectives
By the end of this post, you’ll:
- Understand and calculate the range, interquartile range (IQR), variance, and standard deviation
- Know which measure is most appropriate in different contexts
- Learn how examiners expect you to apply these concepts
- See how dispersion links to boxplots, histograms, and normal distributions
📦 1. What Are Measures of Dispersion?
These are statistical tools used to describe the spread or variability in a dataset.
| Measure | Description |
|---|---|
| Range | Difference between highest and lowest values |
| IQR | Range of the middle 50% of the data |
| Variance | Average of squared differences from the mean |
| Standard Deviation | Square root of the variance |
🔹 2. Range — The Simplest Measure
Formula: Range=Maximum−Minimum\text{Range} = \text{Maximum} – \text{Minimum}Range=Maximum−Minimum
📌 Example:
Data: 12, 15, 18, 22, 25
Range = 25 − 12 = 13
✅ Quick and easy
❌ Very sensitive to outliers
🔹 3. Interquartile Range (IQR)
The IQR shows the range of the middle 50% of values — it’s not affected by extreme values, which makes it more robust than the range.
Formula: IQR=Q3−Q1\text{IQR} = Q_3 – Q_1IQR=Q3−Q1
Where:
- Q1Q_1Q1 = 25th percentile
- Q3Q_3Q3 = 75th percentile
📌 Example:
Data: 10, 12, 15, 18, 20, 25, 30
Q1 = 12, Q3 = 25 → IQR = 25 − 12 = 13
✅ Best for skewed data or box plots
✅ Used heavily in IB, AP, and A Level visual analysis
🔹 4. Variance — Squared Spread
Variance is the mean of the squared deviations from the mean.
Formula (for sample): s2=1n−1∑(xi−xˉ)2s^2 = \frac{1}{n-1} \sum (x_i – \bar{x})^2s2=n−11∑(xi−xˉ)2
Where:
- xix_ixi = each data point
- xˉ\bar{x}xˉ = mean
- nnn = number of data points
This gives a sense of how “spread out” the values are from the mean — in squared units.
🔹 5. Standard Deviation — The Star Player
Standard deviation (SD or sss) is the square root of the variance, and the most commonly used measure of spread.
Formula: s=1n−1∑(xi−xˉ)2s = \sqrt{ \frac{1}{n – 1} \sum (x_i – \bar{x})^2 }s=n−11∑(xi−xˉ)2
📌 Example:
Data: 2, 4, 6, 8
Mean = 5
Squared deviations: (9, 1, 1, 9) → variance = 20/3
SD = √(20/3) ≈ 2.58
✅ Widely used in statistics, probability, and data science
✅ Interpreted in real units, unlike variance
📊 Visual Comparison
(Insert a visual showing two data sets with same mean, different spreads — normal curves or dot plots)
🧩 6. Which Measure Should You Use?
| Scenario | Best Measure |
|---|---|
| Rough estimate | Range |
| Boxplots or skewed data | IQR |
| In-depth analysis | SD or variance |
| Normal distribution | SD (essential) |
💡 Real-Life Example
Two classes both scored a mean of 70 on a test.
- Class A: All students scored between 68–72 → low SD
- Class B: Scores ranged from 40 to 100 → high SD
Even with the same mean, Class B’s performance is more inconsistent — that’s what SD tells us.
📚 Exam Relevance
- IB Math AA/AI HL: Box plots, comparing dispersion, calculating SD with GDC, interpreting variability
- A Level S1: Full manual calculation of variance & SD from raw/grouped data, comparison of spread
- AP Stats: Using technology to compute SD, comparing distributions, discussing variability
📝 Practice Problem
Question:
Data: 10, 15, 15, 20, 25, 30
Find:
- Range
- IQR
- Mean
- Standard Deviation (using calculator)
✅ Try this yourself or book a session for detailed walkthroughs.
🔔 Ready to Master Statistics with Confidence?
💡 Struggling with topics like standard deviation, boxplots, or deeper statistical reasoning?
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