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Why Averaging Averages Gives the Wrong Answer

The average of several group averages equals the true overall average only when every group has the same size — otherwise you must weight each group's average by its size, or you silently get the wrong number.

A trading desk has two teams. Team A trades 90 contracts a day and averages $10 P&L per contract. Team B trades 10 contracts a day and averages $100 P&L per contract. What's the desk's average P&L per contract? "Average the two averages" gives 10+1002=55\dfrac{10+100}{2}=55, i.e. $55 — but that's wrong, and not by a rounding error. The desk actually made 90(10)+10(100)=900+1000=190090(10) + 10(100) = 900+1000=1900 dollars across 100100 contracts, an average of $19 per contract, less than half the naive answer. Averaging averages silently assumes every group is the same size, and here they aren't even close.

The idea: weight by group size, not by group count

The true overall average of n1n_1 items averaging xˉ1\bar x_1 and n2n_2 items averaging xˉ2\bar x_2 is the weighted average:

xˉ=n1xˉ1+n2xˉ2n1+n2.\bar x = \frac{n_1 \bar x_1 + n_2 \bar x_2}{n_1 + n_2} .

In plain English: you're re-deriving the total (each group's average times its own size) and dividing by the true total count — not treating the two group averages as equally-weighted data points. Simply averaging xˉ1\bar x_1 and xˉ2\bar x_2 is only correct in the special case n1=n2n_1 = n_2, where the weights happen to cancel out to 12\frac12 each. The moment group sizes differ, "average of averages" quietly overweights the smaller group and underweights the larger one.

Worked example 1: the desk P&L, done correctly

Using the weighted formula: xˉ=90(10)+10(100)90+10=900+1000100=1900100=19\bar x = \dfrac{90(10) + 10(100)}{90+10} = \dfrac{900+1000}{100} = \dfrac{1900}{100} = 19, i.e. $19. Team A, despite the lower per-contract average, dominates the true blended number because it trades nine times the volume — the naive $55 answer treated Team B's ten contracts as if they carried the same weight as Team A's ninety, which overstates Team B's influence by 9×.

Worked example 2: the classic "average speed" version

A car drives from A to B at 30 mph and returns from B to A at 60 mph, the same distance each way. What's the average speed for the round trip? Averaging the two speeds gives 30+602=45\frac{30+60}{2}=45 mph — wrong, because the car spends more time at the slower speed, not more distance. For a distance dd each way, the outbound leg takes d/30d/30 hours and the return leg takes d/60d/60 hours, so average speed =total distancetotal time=2dd/30+d/60=2dd/20=40= \dfrac{\text{total distance}}{\text{total time}} = \dfrac{2d}{d/30+d/60} = \dfrac{2d}{d/20} = 40 mph — the harmonic mean of the two speeds, not the arithmetic mean, because time (not distance) is what's being equally split here.

Team A avg \$10 Team B avg \$100 naive avg \$55 weighted (true) \$19
Naively averaging the two group averages (\$55) overweights the low-volume, high-average team; the volume-weighted average (\$19) reflects the true blended P&L.

Only average two group averages directly when the groups are the same size. Otherwise compute the weighted average — each group's average multiplied by its own size, summed, and divided by the total size — or you'll systematically overweight whichever group happens to be smaller.

Watch for the disguised version: "average speed," "average return per trade," or "blended yield" all secretly ask for a weighted average (by time, by trade count, by dollar exposure), and naively averaging the individual rates is one of the most common wrong answers given under interview time pressure.

Related concepts

Practice in interviews

Further reading

  • Wasserman, All of Statistics, ch. 3
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