Mean and Standard Deviation
As a follow-up to yesterday’s post, here’s a poem titled Mean and SD by Norman Chansky, professor emeritus at Temple University. Ostensibly, the poem first appeared in the Journal of Irreproducible Results, though I was unable to find an exact citation.
The mean is a measure of location,
The center of a population.
If at random a score you drew,
The mean’s the most likely score you’d view.
You can compute the mean in your slumber:
Sum the scores, and divide by the number.
At the mean, sample scores converge;
From the mean, these scores diverge.
Near the mean, the scores are many.
In the tails, there are hardly any.
But to measure a distribution’s variation,
From the mean, find each score’s deviation.
Each difference of D score, now you square.
Sum all D scores, all scores’ share.
Now this sum, divide by N.
That’s V, the variance, then.
The square root of V is called SD,
The gauge of a trait’s variability.
We’ve found two moments of a distribution,
Developed from each score’s contribution.
Picturing a universe, try to see:
Its center, the mean; its orbit, SD.