I recently came across the following computational trick: to estimate , use

,

where is the closest perfect square to . For example,

.

I had not seen this trick before — at least stated in these terms — and I’m definitely not a fan of computational tricks without an explanation. In this case, the approximation is a straightforward consequence of a technique we teach in calculus. If , then , so that . Since , the equation of the tangent line to at is

.

The key observation is that, for , the graph of will be very close indeed to the graph of . In Calculus I, this is sometimes called the linearization of at . In Calculus II, we observe that these are the first two terms in the Taylor series expansion of about .

For the problem at hand, if , then

if is close to zero. Therefore, if is a perfect square close to so that the relative difference is small, then

.

One more thought: All of the above might be a bit much to swallow for a talented but young student who has not yet learned calculus. So here’s another heuristic explanation that does not require calculus: if , then the geometric mean will be approximately equal to the arithmetic mean . That is,

I'm a Professor of Mathematics and a University Distinguished Teaching Professor at the University of North Texas. For eight years, I was co-director of Teach North Texas, UNT's program for preparing secondary teachers of mathematics and science.
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