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Saying the average is 50,000 dollars can mean three different things. Picking the wrong kind of average is how statistics starts lying to you.
Say a neighborhood's average income is 100,000 dollars. That could mean: everyone earns 100k, or 9 families earn 50k and one tech founder earns 550k. Same average, wildly different realities. Three different averages solve this ambiguity.
| Statistic | How to compute | When it lies |
|---|---|---|
| Mean | Sum ÷ count | When outliers exist |
| Median | Middle value when sorted | Rarely lies; robust to outliers |
| Mode | Most common value | Useless on continuous data |
import numpy as np
from scipy import stats
salaries = [40_000, 45_000, 50_000, 50_000, 55_000,
60_000, 65_000, 70_000, 80_000, 550_000]
print('Mean:', np.mean(salaries)) # 106,500 (misleading)
print('Median:', np.median(salaries)) # 57,500 (typical)
print('Mode:', stats.mode(salaries)) # 50,000 (most common)Three averages of the same dataWhen in doubt, report all three plus percentiles. The 25th, 50th (median), 75th, and 95th percentiles together tell a much richer story than any single statistic. A boxplot visualizes this in one picture.
The big idea: average is not one thing. Picking the right statistic is half the battle in honest analysis.
15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-mean-median-mode
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