a perfectly normally distributed sample

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.

7 Responses to “a perfectly normally distributed sample”

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  2. […] an (ironic?) “Thanks, most helpful!”, this blog post got published, which emphasize on the irrelevance of the r prefix in the function name. Thanks to […]

  3. Alan Benson Says:

    The function mvrnorm has a similar idea using the empirical parameter in its function call.

    • Thank you. I do not think this is the same thing. For mvrnorm, the empirical mean and variance are used or kept, but the produced sample remains random. In this “perfect” function, randomness has completely vanished.

  4. There must be some confusion here… How could that be a random sample? I tried to install the library, but had no success.

  5. Cody L. Custis Says:

    Thanks for the post. I was wondering how the more perfect sample was generated as well, turns out, it s a deterministic function!

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