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Basics: Significant Figures

After my post the other day about rounding errors, I got a ton of
requests to explain the idea of significant figures. That's
actually a very interesting topic.

The idea of significant figures is that when you're doing
experimental work, you're taking measurements - and measurements
always have a limited precision. The fact that your measurements - the
inputs to any calculation or analysis that you do - have limited
precision, means that the results of your calculations likewise have
limited precision. Significant figures (or significant digits, or just "sigfigs" for short) are a method of tracking measurement
precision, in a way that allows you to propagate your precision limits
throughout your calculation.

Before getting to the rules for sigfigs, it's helpful to show why
they matter. Suppose that you're measuring the radius of a circle, in
order to compute its area. You take a ruler, and eyeball it, and end
up with the circle's radius as about 6.2 centimeters. Now you go to
compute the area: π=3.141592653589793... So what's the area of the
circle? If you do it the straightforward way, you'll end up with a
result of 120.76282160399165 cm2.

The problem is, your original measurement of the radius was
far too crude to produce a result of that precision. The real
area of the circle could easily be as high as 128, or as low as
113, assuming typical measurement errors. So claiming that your
measurements produced an area calculated to 17 digits of precision is
just ridiculous.

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