Nice video. I'm a linguist and currently trying to learn Bayesian statistics. Our aim is to model how people choose what to say given the situation of the utterance. It is pretty hard, and I wish I have studied Bayes in school. It would make our lives so much easier.
Great to see that at least one thinking mind has come to a conclusion that spreading the knowledge about the Bayesian statistics is worth to try. Many thanks for that! I had finished my master thesis in psychology at University of Warsaw and I am really shocked how little knowledge of statistics psychologists/ psychiatrists have (yes, non-psychologists, this topic is consisted of 70% of statistics, methodology, testing, experiments, designs etc. and only in 30% of ideas of such individuals as Freud, Horney, Maslow or Pavlov). 5 years of studying this field have given me two sad conclusions about methodology of verifying new thesis of psychology: 1. Even psychology PhDs/ professors (globally, not only in Poland) have just a tiny tiny (if any!) knowledge of basics of statistics (no one, except statistic tutors, cares about normal distribution, skewness of the frequencies, size of the sample and type of the sample when it comes to analyzing the data). As I was helping other students to deal with statistics, I was facing a really shocking attitudes towards stats from PhDs/ professors like "hey, I don't have a clue on what to do with gathered material, so let's do anything - correlation (r Pearsons test) or causation (t Student's test), whatever". That freaked me out a bit as I started to think that really so little psychology PhDs/ professors really know what they are talking about in their papers... Moreover, as I was looking for some literature for my masters, about 50-70% of ALREADY PRINTED papers was a garbage data IMO. Like "hey, I just did a research on 50 Iranian/Polish/American (any nation is applicable) students from 1st year - 35 female and 15 male. I pushed it through the SPSS machine (pushed some buttons and some numbers occur, yay!) and the conclusion is that generally speaking FEMALE are more open to xxx than man (place whatever you want instead of "xxx" (and yet - please don't make me write every mistake made in this description because it will take me another 15 minutes to summarize it :) ). 2. (Which is the result of point 1) If such honored people around the world rarely cares about the key assumptions to be fulfilled, how come a. other students would be able to learn stuff? b. how come these students will be able to verify the meaning of their data? c. therefore, how future thesis would be verified if we both get rid of any theoretical assumptions and forget about statistical knowledge? d. what comes next? Another topic is also the machine of printing scientific papers and the silence of the experiments that did not fit to the already assumed thesis (or those which just crushed the thesis), but it is a whole new area of discussion... :)
I’m very impressed by stating the finite second moment condition that you need for a random variable to be in L2 and you need for many-if not all-of the laws of large numbers and the Central Limit Results.
Yes! Bayesian statistics! I was fearing that this series (like - sadly - so much of modern science) would be all about frequentist statistics. I'm so glad to be proven wrong!
I think you made a mistake while calculating the probability of true positives at 7:42 . The true probability is 48.6%. (450 divided by 925 = 0486486… ~48.6%) To get all the numbers you should start from the bottom of the problem and work your way up: 1) you get the drug users by multiplying the baserate (5%) with all users (=simulations =10.000) --> 500 people are drug users in real life 2) you get the non-drug users by subtracting the drug users (500) from all the users (=10.000) --> 9500 people are non-drug users in real life (=they are clean) 3) you get the true negatives by multiplying the specificity (95%) with the non-drug users (9500) --> 9025 people are non-drug users according to the test and they are non-drug users in real life (=they are clean) 4) you get the false positives by subtracting the true negatives (9025) from the non-drug users (9500) --> 475 people are drug users according to the test, even though they are not in real life 4) you get the true positives by multiplying the sensitifity (90%) with the drug users (500) --> 450 people are drug users according to the test and they are in real life 5) you get the number of positive tests (true positive (450) and false positive (475)) by adding those numbers together --> 925 people are drug users according to the test, independent if they are or aren't in real life. 6) At last you have to divide the true positives (450) by the number of positive tests (925) to get the probability of a person to be indeed a drug user if the test says so.
Wow, this visualization is very helpful. More teachers and resources need to use this method because it helps understand so much more than just a formula.
Just a brief note: If you run the simulation, in the last line of the code "hist(simulated_samples, xlim = c(0,100),breaks=seq(0,100,1))" add a space before "breaks" and after the comma which preceeds "breaks". Otherwise, the code will give you an error.
This makes me think about the false dichotomy fallacy a lot, since we tend to reduce probabilities into the simplest terms, like 50%, instead of more complex and harder to visualize mathematics.
Hello Mr. John Green, My group members and I have currently finished reading Looking for Alaska. We’re working on a English project regarding your book. We would like to interview you. Can you tell us your thoughts about underage drinking and drunk driving?
"As long as the distribution doesn't have infinite variance". And even then it still works... As long as the distribution doesn't have infinite expectation is the real condition.
*_...this, illustrates, even paradigms, the trouble with statistics-by assuming, statistics, is a valid metric or basis and then doing more-statistics on top of that assumption meanwhile mathematics itself consists of finding equivalences of logical and arithmetical-statistics drags a tangent like a ball-and-chain on an average without respect to cause-and-effect... (We still don't have proof that statistics spans the data completely losslessly invertibly)..._*
Good vid. But the drug testing example isn't going to age well when even the US looks back and realises what a grotesque violation of human rights it was. Creepy AF for those of us who live in countries with rights.
I reaaaally dislike the fact that people look at "failing" a drug test as some kind of proof that the person is "on drugs" in the sense of being an addict or corrupt in some way. If I am fulfilling my duties as an employee it's of no business to the company what I do to my body at home. Fyi, I'm not a cannabis user or anything but I very rarely take psychadelic or MDMA. If a company were to take me on a surprise drug test at an unlucky time for me and I would be fired, I would consider it a grave injustice.
If you mean Decartes, my whole point is that even though we pronounce his name the French way, "day-CART", things pertaining to him are not "CARTS-ian" but "car-TEE-shun". Likeways, Bayes' name may be pronounced "bays", but things pertaining to him are not "BAYS-ian" but "bay-EE-shun".
Hey! We need a crashcourse on lord of the rings ! It is such an intricately designed plot executed with amazing imagery. But it cannot be denied that it’s a difficult one to understand. Crashcourse literature would help!
Crash Course is about academic subjects (such as literature in general), not indulging your own favorite fantasy world. There's plenty of other channels dedicated exclusively to LOTR to satisfy you.
Mendicant Bias yeah I get where you’re coming from. But it isn’t about just a ‘fantasy world’ you know? The author created a new language, a new universe and then wrote a book about it. It’s more about the way of writing and analysis of the thought process of the author than ‘indulging in fandoms’
It isn't an opinion. It is a fact that it's classified as a work of fantasy (you can check in any library), and a fact that it is one of many works of fantasy (even more easily empirically verifiable). Or, in other words, it's a fact that it isn't counted as an entire academic subject, merely part of the giant universe that is literature. If you happen to find any other crash course series that deals solely with a specific work (LOTR) from a specific genre (fantasy) of a specific subject (literature, which we already have as a general crash course), let me know.
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I'm not a Nigerian prince, and if you give me nothing, I will give you nothing in return... Other than perhaps treatment like you're a human... most of the time.
