🇳🇱
Everything can manifest as a normal distribution under certain circumstances, its called the Central Limit Theory. It isnt that something is "hiding" as a normal distribution, its more tht you created a situation where the data is legitimately normally distributed under your analysis.
There are many many types of distributions and no educated person on the matter would suggest everything is a normal distribution.
If you want an example of the Central Limit Theorem and more specifically why it isnt really fair to say one distribution "hides" in the normal distribution but rather why simply manipulating the circumstances gives rise to a valid normal distribution we can just look at dice as a simple example.
A single die with 6 sides has a uniform distribution among its outcomes, not a normal distribution. In other words, when I roll every side has an equal chance of being the outcome. So rolling a single fair dice has a uniform distribution of outcomes.
However if we **change** the situation and now instead we roll 100 dice at once and add together their values, doing that many times to determine the outcome, with each individual 100-die roll and its sum being the outcome... all of a sudden those outcomes **would** be normally distributed. This is the central limit theorem at work.
No one would say the dice in the second situation is simply the uniform distribution "hiding" as the normal distribution. The situation is different, the experiment is different, it is, legitimately a normal distribution. Its just that the normal distribution arises from **any** other distribution and is the very reason why it is so common. Either way though the normal distribution will accurate predict the outcome in situations it does arise.
@freemo @funny https://threadreaderapp.com/thread/1008701994897428480.html