On Normalization

Day 3,643, 19:13 • Published in USA USA • by Pfenix Quinn


Well, I got up and walked around
And up and down the lonesome town
I stood a-wondering which way to go
I lit a cigarette on a parking meter and walked on down the road
It was a normal day

Well, I rung the fallout shelter bell
And I leaned my head and I gave a yell
“Give me a string bean, I’m a hungry man”
A shotgun fired and away I ran
I don’t blame them too much though, I know I look funny

Down at the corner by a hot-dog stand
I seen a man
I said, “Howdy friend, I guess there’s just us two”
He screamed a bit and away he flew
Thought I was a Communist



On Normalization


As the SFP pee-pee race approaches, it seems to me that many of the socialist freedom conrads are observing that normalization can have a range of meanings.
Let's evaluate, shall we?


Simply put, normalization means adjusting values on different scales to a notional common scale. In more complex cases, finding the norm means making sophisticated adjustments to bring an entire probability distribution into alignment.

I think you get my drift.







With reference to scoring, then obviously we're talking about a normal distribution. Unless we're dealing with quantiles, in which case we'd seek to bring the quantiles of different measures into alignment.







In statistics, the norm refers to the creation of shifting scales, allowing comparisons of normalized values from distinct datasets in a way that eliminates the effect of certain gross influences.



But.



Normalization can also involve a need to rescale. That is, to arrive at values relative to a change in size.

Then there are norms of ratio, where only rational measures are meaningful, rather than intervals. In other words, the kind of norms sought when the distance between things is not as meaningful as measurements of the ratio.




Finally, let's consider something a bit difficult to measure...


It is said, theoretically, that parametric normalization can lead to pivotal qualities!

That means that a sampling distribution can be observed which does not depend on its parameters. This, in turn, can open the door to even stranger things: ancillary statistics, where pivotal qualities can be computed from observation alone, without even knowing the parameters. Cool, huh?






To summarize, it's simple, if not obvious: Don't be chicken, guys.



Because there's no need to fear standard deviations.