🎓 Dr. Freemo 🇳🇱 @freemo@social.cleverlibre.org

@Ravenhelm Not a new idea, but a good one. I heard someone propose this, perhaps on the fedi, some time back. Not sure if they ever wound up building it though.

lol, I actually met people who have thought like this before, if you can believe that.

@mur2501

Why would you do that? We would all miss you I know that much.

@icedquinn

It isnt about how large the numbers are. Dice rolled one a time are uniform even if i repeat it 10 million times. Dice rolled in small groups of 2 however immediately become gaussian and can be seen even with small samples.

@funny @js290

@lucifargundam I wouldnt say I am exceptional in that area, in fact I'd say im below average as I need more time to learn skills. But I do calligraphy and have done photography and other things with some limited success.

@Ju_ Yea well people just dont have a fucking clue the difference between a love story and a mental-illness fueled lust story apparently :)

@js290

No, we debunked that idea in the last message where I addressed the Central Limit Theorem, why are you regurgitating shit when we already covered rather plainly why it makes no sense to say that?

The Central Limit Theorem proves that**everything** is gausian when measured in a particular way, even if it is non-gaussian when measured in a different way. I showed earlier how dice when sampled one at a time have a uniform distribution, yet when sampled as summed groups become gaussian. Literally everything becomes gaussian when sampled this way.

So from that example it should be clear why a statement as naive as "Dice are not Gaussian" is absolutely false, like everything else they can be gaussian, but they can also have other distributions applied, depends entirely on how you organize the data you get from it which applies.

So similarly we know as an indisputable law, that "pandemics are not Gaussian" is an outright lie, as we know **everything** is gaussian when measured/organized in a way that it presents as such, and is entirely accurate and valid within that context (just as the gaussian distribution of the dice accurately predicted future rolls).

If you want to make a useful case of something, which you seem to want to to do but are dancing around the bush so as to avoid making a direct provable (or disprovable) statement, well you need to be more specific.

@funny

"I cogito therefore I sum, and I sum to cogito" -- Andromeda

@SEEK

Dawww

I am a dog person, but I love all animals really. Its humans I dislike.

@mur2501

@js290

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.

@funny

@js290

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.

@funny

@js290 Of course not, no one ever claimed they are.. we have over 100 different types of distributions we use to describe different types of random events (random event being the technical term here).

Normal distributions tend to be the easiest to understand and one of the more common ones is all.

@vantablack I hope you find some help or some work.

For whats its worth, I have pretty severe asthema and I learned that asthma is, as odd as it sounds, not a comorbidity for COVID. In other words, it doesnt increase your change of dying. I hope that helps ease your mind a bit if nothing else.

If you want to share your resume I can see if i cant share it with a few people and help find you a job maybe?

Now you get it?

#statistics #math #maths #mathematics @funny

Source: https://africageographic.com/stories/ben-injured-elephant-gets-follow-treatment-bumi-hills/

Not sure how accurate this is, I didn't fact check it. But it honestly wouldn't surprise me given the rather advanced behaviors I've seen from animals, especially elephants, in the past.

#Animals #Elephants #Wildlife #Conservation #Conservationism

@academicalnerd Sounds like fun, shame I wont be able to read it. Best of luck!

@academicalnerd Which novel?