The most important skill in statistics

No, it's not gambling

Stay updated with the channel and some stuff I make!

[TheZectorian]

When you mentioned the Cauchy not having a mean it through me for a loop. I had never thought about how the integrals involved in computing an expectation values can just... not converge and the quantity just isn't defined for that distribution.

[msiec]

Came across the video by accident but will definitely stay for longer. I'm honestly surprised that such a good video with clear explanation of the topic has such a small amount of views, definitely deserve more. Keep up the great work!

[ElNick]

You have a genuine talent for distilling the essentials of a topic and explaining them clearly and succinctly. Wonderful work!

[MrNitroklaus]

Great video.
One note on the part with the supercomputers: I have the feeling that statisticians' code can oftentimes be sped up by quite a bit if one just takes efficiency into account. In a vectorized language like R, you want to avoid looping over vectors and dataframes. For example, your central limit theorem simulation can work with much more samples, for example as follows:
```
N <-
observations <- rcauchy(N)
xbars <- cumsum(observations) / seq_along(observations)
number_plot_points <- 1000
indices <- seq_len(number_plot_points) * N / number_plot_points
plot(indices, xbars[indices])
```

[atlas]

Great video. Would love one explaining markov chain monte carlo methods. Another place where assumptions can be sneakily violated is the CLT because it assumes finite variance, so the standard cauchy distribution again gives a counterexample.

[SkegAudio]

For my senior project for my Petroleum Engineering degree, at first i used Monte Carlos simulations to have a proximation of oil and gas production rates using the surrounding offsets (surrounding wells or with similar features) data. It was awesome 👌

I then created a machine learning model for more accurate forecasting:)

[mgostIH]

One issue I have with this video is that it first describes statistics as in "We observe reality and make inference about parameters and our models from it and refine it in a feedback loop", which is a completely bayesian framework, , while in practice you then explain frequentist methods (like estimating the power, type 1 or 2 errors, and even the usage of the law of large numbers isn't entirely correct for that type of inference).

It's not your fault that statistics is often taught like this, but the philosophical framework you think you've setup is different from the one you actually use, leading to confusion in the long run, like what does it really mean to do a null hypothesis test under the standard framework? If I can rule out something, can I also rule **in** that some parameters are a certain way? At the end of the day, what's the optimal way of deciding things about reality? Standard frequentist tests won't answer that for you.

If you think that the methods you're using allow you to say "Oh, given this data (even simulated) the parameter must be within this range" then you've been misled and should rather search for bayesian methods.

[rio_agustian_]

Monte Carlo is well knwon as a method to simulate the behavior of particle in physics. The most popular particle transport software is Monte Carlo N Particle (MNCP), developed under The Los Alamos National Laboritory. Yes, it's where the Project Manhattan took place. In fact, the MC simulation was invented to overcome the problem when they create such weapon.

It's really fascinating to know a method that originally invented as a weapon development during the war now has an immense broad of application in the world like forecasting, pharmaceutical development, finance, radiation science, etc. What a nice method (except for the comically long computational time)!

[yds]

Nice video! I have a PhD in physics, and some of my colleagues use MC very extensively in their work. In some fields this is the main if not the only viable simulation method.

[kevinquiroscanales]

Quite insightful content, yes; but let's not underestimate the importance of good visuals on conveying a message (or simply hooking up new audience). Both of which your channel does wonderfully, great video!!

[dmlarry]

At my university, student managed portfolio analysts have to code and run Monte Carlo on stocks to see all likely moves in price a stock can make based on volatility. It’s crazy

[muhammetmelikkolgesiz]

Thank you for sharing your knowledge! I'm curious and enthusiastic about data and statistics. I'm currently binge watching your videos on my spare time. Keep it up!

[anthonybernardi]

Great video! As a first year Grad Student, I loved the Casella Berger shoutout.

[briskioO]

Chapter 5 Casella & Berger reference goes hard💀

[mikiallen]

I believe you forgot to include "parameter estimation" versus "state estimation" is one of the hottest upcoming skills as well

[drako]

As a software engineering student I always wanted to do this in my stats classes. Just build up all the fancy distributions and tests from first principles (and the bog-standard PRNG you get in every language under the sun.)

Thought it was a crutch, but nice to see that even you galaxy-brain types like to get computational.

[santiagodm]

Love your videos!
It would be good to recommend a book to learn Montecarlo!

[preston]

I implemented Monte carlo sampler for a Raytracer but never fully understood why it works. Great video

[trwbdr]

Recently learnt about MC simulations because they are a key part in testing algorithmic trading systems. Interesting stuff.

[keppr]

Phenomenal work as always, such a succinct explanation and the graphics complement it perfectly!