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.
bayestestRand opened the
rnorm_perfectfunction. 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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