Standard Deviation
Standard deviation tells you how spread out your numbers are, just like variance does — but it fixes variance's biggest inconvenience. Variance comes out in "squared" units (like squared hours or squared dollars), which nobody can picture. By taking the square root of the variance (that's what the √ symbol means — the number that, multiplied by itself, gives you the variance), you bring the measure back into the same everyday units as your original data.
To calculate it in words: work out the variance first (find the mean, subtract it from each value, square those differences, average them), and then take the square root of that result. The answer is the standard deviation, often written as "SD" or with the Greek letter sigma (σ).
A small standard deviation means your values huddle close to the average and are therefore predictable. A large one means they're widely scattered and less predictable. Project managers rely on this all the time in risk and estimating work — for example, it appears in the PERT technique to describe how uncertain a task estimate is. The bigger the standard deviation, the more wiggle room and risk you should plan for.
Think of it like translating a distance from an odd unit back into steps you can actually walk. Variance is like being told an area in square feet, while standard deviation converts that back into plain feet — the same units as the ruler you started with, so the spread finally makes intuitive sense.
Using the earlier example where four tasks took 2, 4, 4, and 6 hours, we already found the variance to be 2. Now take the square root: √2 ≈ 1.41 hours. So the standard deviation is about 1.41 hours. This means that, typically, task times sit roughly 1.4 hours above or below the 4-hour average — a clear, real-world sense of how much the durations tend to vary.
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