Standard Deviation Means Typical Distance
The standard deviation (SD) is the typical distance a value sits from the mean.
- Small SD → values huddle near the mean
- Large SD → values spread far from the mean
It is a distance: always positive, same units as the data.
Standard Deviation: Steps One and Two
Step 1 — deviations from 8:
Step 2 — square them:
Standard Deviation: Steps Three and Four
Step 3 — average the squares (the variance):
Step 4 — take the square root:
Say the Standard Deviation in Words
For 4, 6, 8, 10, 12, the SD is about 2.83:
The values sit about 2.83 units from the mean of 8, on average.
- For large data sets, a calculator or spreadsheet does the arithmetic
- Your job is knowing what the number means
Same Mean But Different Spread
- Both sets have mean 8
- Top SD
; bottom SD — bottom is more consistent
Quick Check: Which Is More Spread?
Two sets, both with mean 8:
- Set 1: SD
- Set 2: SD
Which is more spread out?
Think before advancing.
Answer: Set 1 — a larger SD means values sit farther from the mean. Bigger SD means more spread, not bigger or better.
Your Turn: Build the SD
For the set 2, 4, 6, 8, 10:
- Find the mean
- Find each deviation, then square it
- Average the squares
- Take the square root — say what it means
Work all four steps, then advance.
Answer: Mean
The Standard Deviation Used Every Value
Building the SD relied on the size of every value:
- Deviations, squares, the average — all depend on magnitude
So here's the question that drives the rest of this lesson:
What happens to these statistics if one value goes haywire?
Recall the Median and the IQR
From the last unit, for ordered data:
- Median — the middle value (for 4, 6, 8, 10, 12 it is 8)
- Five-number summary — min, Q1, median, Q3, max
- IQR
— width of the middle 50%
These depend on position, not size.
Predict: Change the 12 to a 60
Keep 4, 6, 8, 10, and change 12 → 60.
Predict how each statistic responds:
- Mean — big change or small?
- Median — big or small?
- SD — big or small?
- IQR — big or small?
Commit to a prediction for each, then advance.
Reveal: Mean Jumps, Median Holds
- Mean:
— dragged far right - Median:
— unmoved
Is 17.6 a good "typical" value? No — most values are 10 or below.
Reveal: SD Balloons, IQR Steady
After changing 12 → 60:
- SD:
over 20 — balloons - IQR: nearly unchanged — the middle 50% never saw the 60
One spread statistic panics at the outlier; the other stays calm.
Which Statistics Resist the Outlier?
| Statistic | Response to the outlier | Type |
|---|---|---|
| Median | barely moved | Resistant |
| IQR | barely moved | Resistant |
| Mean | jumped | Non-resistant |
| SD | ballooned | Non-resistant |
Matched pairs: mean with SD, median with IQR.
Why the Median Held Steady
The median reads position; the mean reads magnitude.
- To the median, 60 is just "the largest value" — same role 12 had
- Its size is irrelevant; it could be 60 or 6000
Position-based statistics resist outliers because size doesn't change position.
The Range Is Maximally Non-Resistant
The range = max − min uses only the two most extreme values.
- For 4, 6, 8, 10, 12: range
- Change 12 → 60: range
, but IQR barely moves
The range lives among the very values an outlier hides in.
Your Turn: The Outlier Stress Test
For 10, 12, 14, 16, 18, then change 18 → 90:
- Note the mean, median, SD, and IQR
- Recompute after the change
- Report which moved and which held
Predict using the pattern, then check.
Answer: Mean and SD jump; median and IQR hold again.
Full Task: Compute, Then Disrupt
For 5, 7, 9, 11, 13:
- Compute the mean, median, SD, and IQR
- Change 13 → 50 and recompute all four
- Which pair stayed resistant?
Do the whole task unaided, then advance.
Answer: Mean 9 → 16.4 and SD balloon; median 9, IQR 4 hold.
Key Takeaways From This Lesson
✓ SD is the typical distance from the mean
✓ Median and IQR resist outliers; mean and SD do not
✓ Use matched pairs: mean–SD, median–IQR
SD measures spread only — not size
The range uses only two extremes
Next: which pair to report, and comparing fairly.
Click to begin the narrated lesson
Compare center and spread of data sets