My opinion on Bayesian statistics is it flows naturally from a Bayesian philosophical perspective but since no philosophy is universally accepted among humans, Bayesian statistics is deeply in conflict with how many people see the world. Whether this is good or bad, I don't know.
Frequentist statistics is grounded in frequencies of events. Since all humans can (objectively?) observe events and count them, Frequentism starts out from a more simple, less philosophy-dependent position.
People can and do add their own philosophies on to Frequentism which can make it seem a bit philosophically confused and incoherent. I think this incoherence and confusion is what Bayesians are often reacting to when they critique Frequentism.
In my opinion, the truth is that Frequentism doesn't officially include an aspect to it that corresponds to Bayesian philosophy (epistemology). So, from the Frequentist perspective, most Bayesian critiques are purely philosophical and fall outside of the domain of statistics.
Frequentism is fundamentally a less philosophically ambitious approach to statistics than Bayesian statistics.

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More from @kareem_carr

13 May
how it started how it's going
apparently you can just buy mushroom growing kits on internet. these are called pink oysters. imma bout to fry these bad boys up. will keep you folks posted.
a little butter, garlic, salt, pepper. not going to lie. i’m having mixed feelings about cooking and eating my children...
Read 4 tweets
7 May
People ask me about getting started with data science all the time. So I came up with three paths for self study: Easy, Medium and Hard.

Continue reading to hear about each path and see my book recommendations. 🧵👇
EASY PATH. On this path, you will get lots of practical skills but only an intuitive sense of the theory. I think the easiest way to start is to read a good data science book and get your hands dirty. I like to recommend "R for Data Science" for that.
MEDIUM PATH. This path gives you some theory with an eye toward applications. A lot of books at this difficulty level suck. The formatting and writing are often bad and the authors are clearly phoning it in. I like "Think Stats" because it's not like that.
Read 6 tweets
16 Apr
live footage from boston. lol. why do i even live here? 😂
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here's the link. it's pretty simple. just a concrete bowl that you can pour rubbing alcohol into. (i'm not getting any money to share this. unfortunately. 😅. ) amazon.com/colsen-Tableto…
Read 4 tweets
5 Apr
This thread walks you through a concrete example of how an algorithm can learn racism. It uses some math but only the minimum amount of math possible and has lots of pictures. It is *very* accessible. If that sounds like your thing read on. 🧵👇
Let's start by learning about statistical bias. Statistical bias is a measure of how well a guessing algorithm is at guessing. It's very straightforward. The bias is the average difference between what an algorithm guesses a value is and what that value actually is. 2/11
The example I'm going to talk about is an algorithm that learns how to measure feelings based on text. We call this measurement a "sentiment score". 3/11
Read 13 tweets
4 Apr
I recently saw some reporting by a white conservative journalist which alleges that black Americans don't value education and I wanted to share my personal experience with that as a non-American black man. In 2018, I graduated with a masters in Biostatistics from Harvard. 🧵👇🏾
Traffic was bad on graduation day so I ended up walking through the streets in my full cap and gown. The thing I remember most about that day is all the happy black faces that were congratulating me. Boston is mostly white so it really stood out. 2/6
Black people were literally congratulating me in the streets as I walked passed. Black people that I didn't know were honking their car horns. It clearly meant a lot to all of these people who were probably on their way to work or doing errands. 3/6
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1 Apr
As a black man, I'm concerned about the tendency for algorithms to exhibit what looks like racial bias. As a statistician, I'm naturally drawn to investigate why this happens But what is "bias"? Surprisingly, the answer depends on what you think it means to be "rational". 1/7
We can think of bias as a type of irrational behavior. So broadly speaking, there are two ways one could define bias in algorithms and this arises from the two major definitions of rationality. These are epistemic rationality and instrumental rationality. 2/7
Epistemic rationality is defined as the part of rationality which involves achieving accurate beliefs about the world. Instrumental rationality is the art of choosing and implementing actions that steer the future toward outcomes that you want. 3/7
Read 9 tweets

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