The test almost never makes you calculate standard deviation by hand — instead, it asks you to COMPARE the spread of two data sets just by looking at them. Knowing what 'spread' means is the whole game.
A number line from 0 to 20, with a tick at every integer.
The segment from 9 to 11 is shaded, with filled circles (endpoints included).
Marked points at 9, 10, 10, and 11.
Values clustered tightly around the mean (10) — small standard deviation.
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Description of the figure:
A number line from 0 to 20, with a tick at every integer.
The segment from 4 to 16 is shaded, with filled circles (endpoints included).
Marked points at 4, 7, 10, 13, and 16.
Same mean (10) but values spread far apart — large standard deviation.
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Quick check
The ages of 7 children in a class are 6, 8, 8, 9, 10, 11, 12. What is the median age?
Worked examples
Example 1
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Example 2
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Example 3
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Common pitfalls
Confusing the mean with the spread
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Trying to compute SD by hand
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Forgetting that identical values give SD = 0
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Mixing up range and standard deviation
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Key takeaways
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Tracks your progress across lessons.
Skill check
10 questions on One-Variable Data: Distributions and Measures of Center and Spread, from our question bank. Score 70% or better across 10 answers to reach Silver and pass this step.
Standard deviation is a number that tells you how spread out a set of data is. That's it. It measures, on average, how far each value sits from the mean (the average).
Think of two basketball teams whose players average 6 feet tall. Team A: everyone is between 5'11" and 6'1". Team B: half the players are 5'4" and half are 6'8". Same mean, but Team B is way more spread out — so Team B has a larger standard deviation.
Here's the key idea the test cares about:
Values clustered close together → small standard deviation.
Values spread far apart → large standard deviation.
All values identical → standard deviation of exactly 0 (no spread at all).
The mean does NOT tell you about spread. Two data sets can have the same mean but totally different standard deviations. So if a question shows you two lists with the same average and asks which has greater standard deviation, you're looking at how far the values stray from the center — not the average itself.
You'll also see range, which is just max - min. Range and standard deviation usually move together (wider range often means bigger spread), but range only uses the two extreme values while standard deviation uses all of them.
The formula exists, but you almost never need it on the test:
standard deviation = sqrt( average of (each value - mean)² )
In words: find the mean, find how far each value is from the mean, square those distances, average them, take the square root. The squaring is why values far from the mean blow up the standard deviation fast.
What the test actually wants:
Compare two data sets — which is more spread out? (No calculation needed — just eyeball the clustering.)
Predict the effect of adding/removing a value — adding an extreme value increases SD; adding a value near the mean decreases it.
Sometimes it bundles SD in with mean, median, mode, and range (measures of center and spread).
Master the intuition — "tight = small, spread = large" — and most of these questions become instant.
Data set X: 10, 10, 10, 10, 10. Data set Y: 4, 7, 10, 13, 16. Both have a mean of 10. Which statement correctly compares their standard deviations?
Two classes took the same quiz. Class A scores: 70, 72, 74, 76, 78. Class B scores: 50, 62, 74, 86, 98. Both classes have a mean score of 74. Which class has the greater standard deviation, and why?
A data set is 20, 22, 24, 26, 28 (mean = 24). A new value is added to the set. Adding which value would DECREASE the standard deviation the most?
Two data sets can have the exact same mean but completely different standard deviations. The mean tells you the center; standard deviation tells you the spread. Never assume equal means imply equal SD.
The test rarely needs the formula — it wants you to compare spreads visually. If you find yourself squaring a dozen differences, you're probably overcomplicating it. Just compare how clustered vs. spread the values are.
If every value in a set is the same, there is zero spread, so the standard deviation is exactly 0 — not the value itself. A constant data set is the smallest possible SD.
Range only uses the highest and lowest values, while SD uses every value. They usually trend together, but a single outlier can blow up the range without matching SD's response. Read which one the question asks for.
Standard deviation measures spread: how far, on average, values fall from the mean.
Tightly clustered data = small SD; widely spread data = large SD; all identical values = SD of 0.
Equal means tell you nothing about standard deviation — always compare the spread separately.
Adding a value near the mean lowers SD; adding an extreme value raises it.
The test almost always wants comparison/intuition, not the actual formula calculation.