a perfectly normally distributed sample

May 8, 2019
By

(This article was first published on R – Xi'an's Og, and kindly contributed to R-bloggers)

When I saw this title on R-bloggers, I was wondering how “more perfect” a Normal sample could be when compared with the outcome of rnorm(n). Hence went checking the original blog on bayestestR in search of more information. Which was stating nothing more than how to generate a sample is perfectly normal by using the rnorm_perfect function. Still unsure of the meaning, I contacted one of the contributors who replied very quickly

…that’s actually a good question. I would say an empirical sample having characteristics as close as possible to a cannonic gaussian distribution.
and again leaving me hungering for more details. I thus downloaded the package bayestestR and opened the rnorm_perfect function. Which is simply the sequence of n-quantiles
stats::qnorm(seq(1/n, 1 – 1/n, length.out = n), mean, sd)
which I would definitely not call a sample as it has nothing random. And perfect?! Not really, unless one associates randomness and imperfection.

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