If you’re exchanging information with scientists / engineers you could also provide with every F figure its ±P “precision” (Y% of chance to be in the Gaussian centered on F with k(Y)*P standard-deviation, k to be computed from Y). That way if the person you’re giving information to needs to compute a new statistic, it can combines Gaussian models and have a new (F’ ± P’)
Michael Nelsonsays:
I would add to the statement “serious people will not be so easily fooled.” When I see such precision in reduces my confidence in the source. My internal “bozo” warning light comes on.
I had the concept of significant digits pounded in to my head by my (very excellent) high school science teachers. Now I have an aversion to over-precision.
most excellent post. i recommend gustafson’s book for another angle on digital error.
https://www.amazon.com/End-Error-Computing-Chapman-Computational/dp/1482239868/ref=sr_1_1?s=books&ie=UTF8&qid=1548866338&sr=1-1&keywords=the+end+of+error
Of course you’re right.
If you’re exchanging information with scientists / engineers you could also provide with every F figure its ±P “precision” (Y% of chance to be in the Gaussian centered on F with k(Y)*P standard-deviation, k to be computed from Y). That way if the person you’re giving information to needs to compute a new statistic, it can combines Gaussian models and have a new (F’ ± P’)
I would add to the statement “serious people will not be so easily fooled.” When I see such precision in reduces my confidence in the source. My internal “bozo” warning light comes on.
I had the concept of significant digits pounded in to my head by my (very excellent) high school science teachers. Now I have an aversion to over-precision.