Normally distributed Random numbers generator issue

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I've used the randn function (setting rng(1)) and plotted the histogram:
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the result I've got in Matlab 2020b was rather far from a normal distribution.
Increasing the amount of generated numbers helps a bit - but not enough.
Any ideas on how to get a more adequate, normally distributed, bumbers ?

Answers (4)

Veronica Taurino
Veronica Taurino on 24 Mar 2021
Edited: Veronica Taurino on 24 Mar 2021
The following (but also yours, from my pov) seems quite normal to me, why is it not ''enough'' normal for you?
number = 1000000

Jan on 24 Mar 2021
Edited: Jan on 24 Mar 2021
This is a perfect normal distribution of 500 values. With such a small set of data, a lot of noise is expected. Otherwise it would not be a random normal distribution.
So the only problem is, that you expect something else, which does not match the defition of normally distributed randomness.
So what do you need? Do you want a perfect normal distribution without randomness, e.g. by just shuffling the values?

Stanislav Lifshitz
Stanislav Lifshitz on 24 Mar 2021
The random sequence I've generated was for 500 values, which isn't a small set.
I understand that randomizing a sequence of 1e6 values will generate a normally distributed sequence (There is a theorem in math to support this claim)
However - the analysis I need to do based on these values is quite compuattationally demanding and calculating this for 1e6 values isn't an option.
So - my question is can I get a noramlly disitributed set of 500 values from 'randn' or should I be looking for alternate ways to get a normally distruibuted set ?
Thanks a lot for your thoughts on the matter.
Veronica Taurino
Veronica Taurino on 25 Mar 2021
I totally agree with Jan. You have been confused by the shape of the data, perhaps? But for definition, it is a normal distribution (as Walter has pointed out, mean 0 and std 1). If you just want to change the shape, it is not randon anymore.

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Jeff Miller
Jeff Miller on 24 Mar 2021
As the other posters have said, you aren't necessarily going to get an exactly normal-looking distribution if you generate the numbers randomly. But you can nonrandomly generate an exactly normal-looking set of numbers like this:
nPoints = 500;
cdfvals = (0.5:1:nPoints) / nPoints;
evenRand = norminv(cdfvals);


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