Alternate to Central Limit Theorem

It's Tech Tip Tuesday!

Today, I'm sharing a quick tip in statistics.

This tip is an alternate to Central Limit Theorem (CLT) in statistics.

For those who don't know, CLT is used to satisfy normality in your data set. If you don't have normality, you cannot assume your sample set represents the population. CLT helps by stating that you can have a minimum of 30 samples, and it will satisfy normality.

BUT WHAT IF YOU CAN'T GET 30 SAMPLES?

I get it, as I've seen it on the bench before. I've done this for tensile & elongation testing, compression analysis, flexural modulus testing, running rheology, viscosity, titrations, and all sorts of lab tests.

Follow this method as an alternate to CLT, but only when you cannot follow CLT. And of course, the more samples, the better!

Have you tried this in the lab before?