Matlab: White noise with flat/constant power spectrum I am relatively new to signal processing techniques and Matlab and need a bunch of test data in the form of white noise, as defined on the Wikipedia page, with constant, flat power spectrum. 

I tried the matlab built-ins wgn(m,n,p) and randn(m,n) to get vectors of white noise, and then used different combinations of the periodogram/pwelch/dspdata.psd functions to check what I believe to be the power spectrum. I cannot say I am 100% certain on the basis of documentation and other forum sourcing, so do correct me if I am mistaken.

The resulting plots I get do not look even close to a flat spectrum but instead inherit the "zick zacky" curve of the original random data. The question I inevitably had to ask myself: Do you even know what to expect and how to understand the results of the functions and how a flat power spectrum looks like? I don't, hence I am here, looking for clarification.

I am looking for answers which go beyond the Wikipedia pages etc., as they left me confused.
You need to average a bunch of FFTs of white noise to approach the average power spectral density.

see this and this.