**W**hen 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.

`bayestestR`

and opened the `rnorm_perfect`

function. Which is simply the sequence of n-quantilesstats::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.

*Related*

**leave a comment**for the author, please follow the link and comment on their blog:

**R – Xi’an’s Og**.

R-bloggers.com offers **daily e-mail updates** about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more…