Are you Bayesian or Frequentist?

What if I told you I can show you the difference between Bayesian and Frequentist statistics with one single coin toss?
SUMMARY
It is only by insisting that the parameter may not be a random variable (Frequentist) that it makes any kind of sense to talk about your method's ability to deliver the right answer. As soon as you let the parameter be a random variable (Bayesian), there's no longer any notion of right and wrong.
BAYES VS FREQUENTIST... FIGHT!
Visit my blog (bit.ly/quaesita_fvsb) for answers to the questions below.
What words tell you that we're wading through their territory?
What are they using statistics to change their minds about?
What is the main difference?
What do you win if you choose this side?
What do you lose if you choose this side?
So, which one is better?
Okay... so which one is more objective and "correct"?
What about small vs large sample sizes as a criterion?
Cassie, you're killing us here. Are you Bayesian or Frequentist?
Should I pick a side?
Learn more:
Frequentist vs Bayesian fight - your questions answered: bit.ly/quaesita_fvsb
8min statistics intro: bit.ly/quaesita_statistics
Statistical Thinking playlist: bit.ly/statthinking
Controversy about p-values: bit.ly/quaesita_needles

[@lrgui9792]

I love this subject! I'm studying Bayesian methods in my PhD, here's my perspective: Frequentist reasoning wants to deal objectively with data, so it considers probability to be a property of the world; it says "the coin has probability 1/2 of being heads because that's the frequency of heads in the behavior of this coin"... and there's a right probability, it's a fact from the world, it can be learned by data that shows that frequency in behavior. Data is noisy, but it reveals true propensities through frequencies. Bayesian reasoning wants to deal logically with data, so it considers probability to be a property of logical propositions about the world; it says "the statement 'coin landed heads' has a certain probability of being true, it is 1/2 for me and 0 or 1 for you depending on what you see"... the proposition is connected to a point of view, and different points of view will differ in how close they are to the truth about the state of the coin. So probability is subjective in a sense, but all points of view with equal information should objectively agree about probabilities of their statements (it's objectively subjective, just recognizing the existence of different points of view, but they are not supposed to be personal, not opinions). When you update your "belief" over data, it's because data moved your point of view in relation to the "truth". In this example, once we see the coin, we update the statement "coin landed heads" from 1/2 to 0 or 1 depending on what we see (probabilities of 0 and 1 mean perfect information while 1/2 means no information). There isn't an actual divide between the two, theoretically... Bayesian reasoning recognizes the relation between frequency and plausibility, while frequentist reasoning recognizes points of view, it just doesn't go there.

[@ndrsvgl]

So in other words the frequentists approach will give you a correct answer every time but may need indefinitely to evaluate, while the bayesian approach gives you a result immediately but this result may be wrong.

[@lrgui9792]

@@ndrsvgl hm... the approaches are answering different questions, both correctly... what is this answer/result that you say they are trying to give? in the experiment of the coin, we can arrange for the mutual agreement of the two approaches in every observable event (observable in the sense that it doesn't talk about probabilities)... for example, if you flip the coin and ask for the exact result it will give, both should agree in saying: "I can't tell"... if you say you flipped the coin a zillion times and aks for the proportion of heads, both should say immediately: "it's 1/2" (but they would interpret your question differently)... I guess you are talking about this difference: you ask what is the probability of you having a coin in your pocket. One bayesian could say: "I know nothing, so for me it is 1/2"... another bayesian could say: "I saw you handling coins before, and if I have a model for how probable it is for "coin handlers" to have coins in their pockets, I can update that probability of 1/2"... another bayesian could say: "I know that people in this city carry coins in their pockets with a chance of 30%, so it's 30%"... someone could say: "it is 80%, because I saw in the stars", but that wouldn't be bayesian, it's a personal kind of subjectivity... a frequentist would say: "i can't know, because I don't know anything related to the frequencies with which you carry coins in your pocket, i don't talk about probabilities for single events" now, those bayesians can't be right at the same time, yes? and that frequentist will take a long time to discover the frequency by making the situation become a repeatable experiment... the thing is, all bayesians are right, but their answers have limited power where the frequentist has no answers at all... it may seem silly, but that "imprecise" reasoning of bayesians has major applications, here's a very understandable example: en.wikipedia.org/wiki/Bayesian_search_theory

[@N73B60]

Bayesian and Frequentist are two thinking methods to answer different questions: what I know and what I should know. None are wrong, and it doesn't have to do with psychology or personality. It depends on the situation and which question should be answered first.

[@ndrsvgl]

@@lrgui9792@N73B60 Ok, thanks for clarification.

[@richarddisalvo7952]

My interpretation is that the bayesian reasoning is correct, but the frequentist reasoning is easier for a variety of reasons so scientists often use it instead. I take this comment as empirical evidence for my claim, as the frequentist explanation takes about 1/3 of the words to explain than the bayesian.

[@soonny002]

As a psychiatrist, I feel like I rely on both Bayesian and Frequentist philosophies in my everyday work. When advising on diagnoses, I use the Bayesian approach. That is, I gather whatever data I can to inform an opinion (about a diagnosis), and then I update my opinion if and when more data emerges. I'm not overly invested in getting a 'right' diagnosis because a patient's presentation is dynamic and complex such that they can't always be reduced to a single category at all times. I'm happy to revise the diagnosis when necessary. But when I'm advising on risks (i.e. the risk of somebody committing suicide, homicide, arson, etc.), I use the Frequentist approach. I am infinitely more concerned about what will happen when a patient has had numerous repeated attempts at harming themselves or others because that informs the probability of how likely a person is going to repeat history. I think to myself, "what will happen if the patient attempts the same move another 100 times?" To me (and I'm not a statistician, although I know a little about human psychology), the Bayesian and Frequentist approaches are fundamentally concerned with certainty vs uncertainty. The Bayesian aligns herself with changeable opinions informed by available data, thus she is never completely 'certain' about anything since her opinions change when new data emerges. The Frequentist on the hand aligns himself with unchangeable facts based on logic, thus he is always completely 'certain' about everything as long as his logic holds water. The coin toss was a great teaching example. It was a great example because the answer was inconsequential. I mean... Who cares how the coin lands? Nobody was harmed in the making of the video, yes? (I hope). We can allow ourselves to assign equal weight to both philosophies in this teaching scenario when the outcome of the coin toss was inconsequential. I suspect people are likely to gravitate towards a Frequentist approach when contemplating decisions that are very consequential because the Frequentist approach feels more tangible to me while the Bayesian approach feels more abstract. So I don't think it is a matter of 'are you a Bayesian at heart or are you a Frequentist?' Rather, it may depend on the weight of the decision you are about to make. Having said that, I do acknowledge that some people are more tolerant of uncertainty than others, thus for those people, they are more likely to be Bayesian perhaps. Does this make sense?

[@traininggrounds9450]

It's less about the weight of the decision but the fundamental structure of the question. If you ask, "What is the probability I WILL flip a coin heads?" versus "What is the probability this is heads?" then this is the difference between the two thoughts and it doesn't matter the importance or the pertinence or the weight. So you are right in saying it has nothing to do with personality or thought process its just whether or not people are aware about the two different "modes" if you will of an outcome.

[@ivlis32]

Do not mix human senses with the math. Both Bayesian and Frequentist approaches are mathematically solid and they converge when infinite data is available. F. put the uncertainty into sampling, i.e. we do not know the exact value because we only observe a limited subset of the universe and we can make an estimate of the interval where the true value can be. B. put the uncertainty in the value itself: as we have limited data available (including prior knowledge) we can say that the value in question can be drawn from a distribution. So instead of a point estimate B. give you a distribution of values. There is no more correct or less correct approach, it depends on what you want.

[@harry8175ritchie]

I like this approach! It definitely depends on how you want to model your problem, and, how you interpret the answer. Let's say your treatment is a parameter. The bayesian approach won't tell you what that parameter is, but will provide static confidence intervals of where it lies. More data, and informative priors will change the shape of this distribution. The frequentist approach will tell you the parameter (the treatment), with variable levels of confidence (depending on the data).

[@sgordon8123]

I am completely new to this concept but came down as a Bayesian thinker. I immediately thought however that my acceptance of uncertainty and willingness to make decisions even knowing the uncertainty is an unusual quality in me. It tends to drive others nuts as they prefer to be certain before acting. I guess I simply think certainty is an illusion most of the time. And I would definitely say that about medical risks as the research quality is often so poor or misunderstood or out of date and so on. Are you familiar with Ben Goldacre? If not please check his KZbin lectures.

[@jjmarcos]

Psychiatry is GAY.

[@juancampos1164]

49% heads. - 49% tails - 0.4% a bird steals it - 0.4% it disappears - 0.2% I’m imagining this and it doesn’t exist (but my eyes construct the visual spectrum in my mind)

[@solsav]

I think it has a lot to do with the nature of the question and the search-space in your problem, rather than your personal choice, to go with a frequentist vs bayesian method.

[@yt.abhibhav]

Exactly

[@banana420]

I don't think the framing of Frequentists caring about the true answer is a good one. Bayesians and Frequentists both care about truth, they just care about the true answer to different questions. I think the main difference is that Bayesians and Frequentists ask different questions, and use language that implies the questions they care about, which is what makes it so difficult to have a conversation with the other perspective. When truly asking the same question, the two mindsets should converge on the same answer.

[@milos-simic]

The main difference is how probability is defined. For Bayesians, probability is one's degree of belief. So, it is inherently subjective. For frequentists, probability is a law of the universe that regulates an event's long-term frequency of occuring. So, it's opposite of subjective. All the other differences stem from this one.

[@PACXS]

Very much agree. Wittgenstein would say: arguing for one, against the other, is simply an abuse of language. There is no contradiction. One is not right, while the other is wrong. Those that argue are misled by their own language use. Language is highly indeterminate. "What side is up?" Sounds like a simple and straightforward question, but it is not. It is based on context, the forms of life, and family resemblances. To the question, "what is the chance it is head's up?", it can be interpreted as in what is the chance your guess is correct, or what is the chance the actual state is head's up. In a deterministic universe, the frequentist would not even be able to say there is a 50% chance it lands head's up before the coin has landed. It may not have landed, but how it will land is determined by prior values that have already been set in motion.

[@jeandenis4353]

I agree with banana's comment. For example when I read about zodiac signs, it usually describes the signs strengths and weaknesses. But I've always wondered how can someone who is not of that particular zodiac sign classify or identify a sign's attributes as a weakness? This assumption would imply that you've lived through their experiences.

[@jeandenis4353]

P.S. they're trying to raise robots. I think it's in a Disney movie "Inside Out"

[@redberries8039]

If we say for Frequentists the probability of a system is a 'property of the Universe', a property of the system. Then Baysians allow talk about their belief of that probability, whereas Frequentists restrict themselves to estimates of the probability to calculations on the data. ?

[@rapmacl1]

LOL. Schroedinger's coin.

[@cpmathews2566]

Not only Schrödinger's coin, at 50/50; But when she showed us the coin I could not see it clearly. For me it is still, 50/50. Fore her it's 100%

[@traininggrounds9450]

@@cpmathews2566 You have no concept of the collective. As a collective it was what she said it was both for you and her because we all see the same colors on the rainbow. This schrodinger's cat thing is such a bas tardization of the scientific method and everything that has made progress in science up until that dumb concept came about. It's literally not true. The nature of statistics itself is that we can't determine something or its nature. Schrodinger took the nature of statistics itself and masked physical phenomenon with it and said "this is the fundamental nature of the universe" when the entire time it was relative to us and our ability to know. So statistics and its nature is schrodinger's cat we'll say but not the universe. You may be confused but the universe is certain.

[@bonob0123]

@@traininggrounds9450 relax. quantum bayesianism is a thing. just has its own limitations with locality etc etc.

[@andrewnguyen3312]

Hilarious

[@alexeyzelenskiy2224]

Thank you. Perfect pausing during the presentation. It is so rare among KZbin presenters.

[@IanALane]

Two minutes in to my first Cassie Kozyrkov video and I'm subscribed.

[@marktahu2932]

Thank You.. You are a seriously great teacher, I'd like to see your videos as a necessary part of every high school students curriculum. You remove the jargon and re-frame the learning references to simple, understandable examples which makes the learning of complex issues so much easier.

[@donnachafitzgerald256]

Thanks for summarizing this important topic. I've no emotional investment in the Frequentist vs Bayesian debate (I've used both often in my research), but I couldn't help but feel the Bayesian perspective was not given fair credit here. The Bayesian's "point of view" should not be entirely subjective, but rather based on logical principles, that can often be derived from the laws of nature (eg. The shape and symmetry of the coin make it equiprobable to land on heads or tails). The powerful advantage of leveraging prior information for forecasting or evaluating hypotheses also ought to be emphasized more. Ideally, aspects of both Bayes theorem and frequencies should be used (eg. using base rates as prior information in diagnostics). But because the frequentist approach is so much more intuitive to use in science, the Bayesian approach is underutilized. This has been less than optimal for science which has been far too dogmatic about p-values instead. It would be nice if you could emphasize this more in future communications on this topic, thanks!

[@louisbademosi9250]

Hands down the best video I’ve watched on the philosophy behind both the Bayesian and Frequentist approach. Well done

[@LegoEddy]

How is it possible that I only found this channel now? This stuff is brain-food-candy for any statistician like me! Keep it up :)

[@acethatmaths9053]

I love how you explained the 2 perspectives.

[@P_Mann]

I think it’s important to understand whether your need is action - doing or not doing something in the real world - or just thought/academic. Bayesian tends to promote action, such as our decision to drive more slowly in poor weather. Frequentist tends toward addressing issues where a discrete level of certainty has utility (often without action), such as hypothesis testing.

[@inquinimento]

Your action vs action recommended as an expert interpreting data?

[@SarikaKV]

I am binge watching your videos. Wish I’ve known you and your work before.

[@RafaelRabinovich]

Thank you for your clear explanation!

[@josevalencia9233]

Gracias Ms Cassie. Very well explained. Thanks to YT recommendations as well

[@CodeEmporium]

Loved this!

[@PACXS]

Frequentist: "What is the probability the actual current state of the coin is ..."? Bayesian: "What is the probability my estimate of the state of the coin is ..."? It seems Frequentists try to take an objective point of view. Meaning: from the perspective of the focal object. Whereas the Bayesians take a subjective view. Meaning: from the perspective of the subject/observer.

[@ApesAmongUs]

I like that description more than her use of the word "opinion".

[@zak3744]

The explanation of frequentist thinking in the video sits uneasily with me. The idea that a flipped, but hidden, coin would be either 0% or 100% (but unknown) while an unflipped coin would be 50%, or a coin flipped and waiting to land would be 50% seems arbitrary. It's more subjective than the Bayesian explanation in a way! There are a number of reasons why the outcome might be obscured to us, whether that data is temporally obscured (you have't flipped it yet, and we experience time in one direction!), or physically obscured (a hand is in the way), or obscured through laziness (the flipped coin is in the air waiting to land and we could measure the rotation and velocity and calculate how it will spin and drop and land, but we don't bother). I don't see, on an ideological level, why it should make a difference which of these it is. So if the hidden coin is "either 0% or 100% but I don't know which" to a frequentist, why not the future event coin too? It seems that there is something getting lost in the difference between the questions: "What side *will* this coin land?" and "What side *did* this coin land?" The frequentist as described here doesn't ever change their answer to the first question once the coin has flipped, they just start answering a different question instead, right? As I far off the mark if I were to think that maybe a change in information is being acknowledged in one case explicitly by changing the answer and in the other case implicitly by changing the question?

[@PACXS]

@@zak3744 You are correct in many things you say. In a deterministic view, everything is already determined. So, to a Frequentist this would have to mean that all coins being flipped, either in the future or past, all are determined ... and thus none have a probability. Only abstracted coins have probabilities in this sense. Elsewhere here, I commented about this being really a linguistic difference. The difference is not really about the understanding of reality, but what question is asked. Usually in science the Frequentist question tends to make most sense. It is about the object / objective reality. When you look at it from a decision making agent, it makes sense to look at it from a Bayesian perspective. The question is then about: what is my best estimate based on what I know? Or: the best decision based on the information available to the Agent (e.g., a robot).

[@gcoronado4622]

thank you for this EXCELLENT video!

[@sandEffect]

I like the evolution of these videos into a nice variety of presentation.

[@navarro_gabrielle]

This is AMAZING!! Great video. Thanks for sharing!!!

[@sigbjrnfjelland4686]

What a beautiful philosophical view statistic perspective. Loved it!

[@alanbarnett718]

it occurs to me that most situations where you want to know a probability are Frequentist, i.e. they concern things that are already facts, but unknown to us. From a certain perspective *all* probabilities are Frequentist, if you accept that we live in a deterministic universe. All future tosses of the coin are already predetermined! Love this post!

[@ironnerd8336]

Holy crap, statistics has a place for me. Never heard of Bayesian before. I gotta do some reading now. Thank you! Seriously, that makes so much sense in my world.

[@JHS270694]

Beautifully put. Very intuitive.

[@MrDemoncrusher]

*Neo* "Heads!" *Morpheus* - "What if I told you there was no coin...?"

[@jamescrud]

It seems like it first depends on how a person understands the initial question. I understood it as what's the probability of me guessing if it was heads or tails. Answer is always going to be 50/50. The coin already landed so it was never a question of which way it was resting on your palm. That has already been determined. The only thing left to do is guess the right answer...which is always going to be 50/50. Edit: This was such a random video click for me. Lol.

[@psigreen3864]

This is how I understood it as well. It seems kinda flawed, in a way. Is the way of determining whether you are Bayesian or Frequentist examining how you interpret implications in the question? As I stated in another reply, the question, as I believe is likely to be understood by most, would be "What is the probability [that you could correctly guess] that this coin is heads-up on my palm?" Or is the video saying that Bayensians would assume that she is referring to the guess and Frequentists won't? If that's the case, then Frequentists don't sound very fun at parties... I feel like this determines more about how wiling you are to "go along" with a potentially unclear question. I think, in a more casual setting, a Frequentist is still likely to guess 50% because, in their mind, they are going along with the commonly understood implication of the question, or, in other terms, "trying not to be a smart-ass." Is this psychology or statistics or both?

[@mikebirminghamnz]

I agree. I had to watch it again because it made no sense to me! Her metaphor / analogy / example is poor.

[@rigasok]

It’s the first time one has questioned it to me... great explanation!

[@JohnJones-rp2wz]

Fascinating explanation, thank you

[@robertzavaleta6992]

This was beautiful. Thank you.

[@olesiamaltynbaeva6091]

Cassie, thank you for detailed explanation. Please, please, please tell us more about pros and cons of Bayesian vs Frequentist approaches in the business context.

[@albertolopez3017]

Excellent vídeo! Thanks for this explanation

[@RodrigodaMotta]

It's curious to think about probability and statistics theories in physics. In my perspective, the experimental physicists generally goes with a Frequentist mind, since the common mindset is to try the same experiment over and over and look what percentage they got a "good answer". In the other hand , the theorists care too much about perspective and information, actually the quantum theory is bounded to theses concepts, hence, the resolutions of the problems are made from a Bayesian mind. Your videos are awesome! Loved it!

[@christopherkemsley4758]

This is a fantastic short explanation of the differences between them - thank you! Also, for some reason, you kind of remind me of Kes from Star Trek Voyager…

[@matthewd4743]

I like how you paused after you the coin had landed and you were going over options... I was yelling there is no probability since it's a completion action. I guess I'm a frequentist. Thanks for teaching me something 😁 very good lesson!

[@the_trevoir]

And the Micheal Baysians don’t care I’d it’s heads or tails, as long as the camera orbits it.

[@mattc4013]

And as long as there are pyrotechnics and explosions.

[@MyManinHavanna]

The Baeysian recognizes that the probability is relative to perspective which means recognizing the context of other's perspevtive. Perspective must involve uncertainty. The problem is that it usually loses that component. I think some people combine the two to eliminate their feeling of discomfort.

[@biologygeek3601]

just came across this wonderful video, very simple explanation, read many articles but still confuse with the technical terminologies they have used in the paper. from this video within 5 minutes i have understood the concept. Thankyou Cassie for sharing wonderful knowledge with us. really appreciate it :)

[@AzharKhan-wn8wy]

“The truth has already been fixed in the universe”. Powerful, powerful stuff. 🙏

[@musanovdarkhan5773]

This is the coolest video I've seen in a while!

[@Sau_UNCCMathFin]

Very well explained.

[@kentvandervelden]

I recommend reading the short paper Bayesian Estimation Supersedes the t Test.

[@donrossi4319]

Well done!

[@sojarumojaru]

Loved it!! Theoretical chemist by profession, I daily have to navigate the thin line between the groups of people who either call themselves "Statistical mechanicians" or "Quantum mechanics". I might cite this video in future to demonstrate to my colleagues the philosophical difference between the two. :)

[@KolbiGray]

I just stumbled across Florence Welch talking to me about statistics and I love this.

[@johnjung-studywithme]

that was such a beautiful explanation

[@LuckyNrZ]

Great video and explanation! :) By coincidence, I came up with the exact same example (covered coin toss) to explain the difference between Bayesian and frequentist to my colleagues, and I was also going to use it in a paper I‘m writing. I‘m wondering now, is there a „common source“ of the example that I should be citing?

[@sjn7220]

This is why I’ll never understand statistics beyond mean and standard deviation.

[@iancamleite2285]

Love this!

[@norberelloa9794]

Clear explanation, thanks. Will it be possible to have a mathematical explanation as well, such as doing the same analysis (for instance comparing mean across two groups) using Frequentist (a t-test) versus Bayesian approach (?) ?

[@pezraya5210]

Thanks!

[@havenbastion]

Statistical probability is a measure of ignorance, not certainty. Everything that Does happen always had a 100% chance of happening, we just didn't have enough information to know it. The two perspectives are perfectly compatible. Also, knowledge is justified belief.

[@surfinmuso37]

Agree on the first bit, but not on the second. Knowledge is not simply justified belief, some does occur that way but much is also direct knowledge that does not require a belief.

[@mc.rhyme.rivers]

Great video! What are you recovering from as a statistician?

[@willykitheka7618]

You're a damn good teacher madame! If you are not using that talent somewhere along your path, then it's a crying shame!

[@atticstattic]

That was the coolest thing I've seen this year.....I think....

[@Armando_Lara]

What a charisma!

[@rexbenny1553]

This video, besides sprouting an interest in the philosophy of probability (to put it lightly) and how English sounds to those who don't speak it.

[@vincentpun123]

Thank you!

[@hwilliampolenz6713]

This is really neat, thanks!

[@liammckenna1479]

In my undergraduate statistics courses, I essentially was indoctrinated to think in a frequentist way, to look for the result of a statistical experiment and say that I conclude with 95% confidence (or based on whatever your confidence level was) that a result was significant, or say that I have failed to reject the null hypothesis. So far, I still like that way of thinking.

[@cementhaiku]

THANK YOU so much for this video, and all your videos. Please keep making them. I love to learn and appreciate great teachers. You accomplish both feats...and I don't think a Bayesian or Frequentist would disagree. But maybe they would. lol. Again, THANK YOU.

[@rohanchess8332]

Amazing video!

[@andrewwhite5250]

I think both perspectives can be beneficial in different scenarios, depending on what you’re trying to accomplish.

[@karimnaufal9792]

Cassie, very interesting, which one is best for online A/B testing in your personal opinion?

[@brunosignoriwustro9460]

1:00 I thought it was a trick question and you were going to show the coin stading up, stuck between your fingers, to make a point about how there might not be a probability. Guess that makes me a Bayesian.

[@robmonkriedlinger]

oh wow!! So helpful for me - thanks!!

[@DaNazmul]

Thanks.

[@phg0922]

A better question is, would you go for Monte Carlo simulations or bootstrap draws for small samples 😉

[@666Tomato666]

and what if I do both?

[@troelspedersenaau]

My own research is in electronic engineering and I teach a graduate course on Stochastic Processes to engineers and applied mathematicians. I do research with both statisticians and other engineers, and have thus develop a rather pragmatic view. Losely, I pick the perspective which helps my current project. This leads me to be Bayesian 50 % of the time and frequentist 50 % of the time. (And Iannoy 100 % of the statisticians most of the time... ) I do not think that you should or need to choose between the two views. I think that both views are correct - but it is not the same thing they are looking at! That is probability to a Bayesian models the current uncertanty about an event whereas to a frequentist it models a frequency of correct guesses if the experiment could be repeated. Incidentially, the underlying probability theory (the events, probability measures and all that) applies to both schools. But although the terms (e.g. Probability, expectation, etc) used are interpreted in fundamentally different ways. Many times the two views can lead to the same thing. I pick the view which seems the most helpful in achieving my goal in a certain application. A perhaps even more fundamental concern to have, is the view on probability theory itself. The "Are real things random or not?" questions. As a pragmatic engineer, actually, I do not care - what matters to me is that probabilistic models, are very good and convenient models for many phenomena. Applying statistics be it frequentist or Bayesian, lead to many good ways of guessing on phenomena which cannot be observed directly.

[@philledwith8307]

"That is probability to a Bayesian models the current uncertanty about an event whereas to a frequentist it models a frequency of correct guesses if the experiment could be repeated." -- This feels like a more faithful representation of what's going on, to me.

[@Speak4Yourself2]

Thanks a lot!

[@mrbattowel]

Hidden under your other hand or hidden in the future, is there a meaningful difference?

[@J_The_Colossal_Squid]

This makes sense to me in terms providing fodder for potentially thinking about how better to modify statistics education curriculums in the US, and I thank you for it. I agree they both have their place. My sense is still that Bayesian in general is best, or at least better conceived between the two as being a more efficient process/procedure for studying phenomenological emplacements, such as a state of being, or a state of existence, as distinct from pursuing quantifying outcomes from an empirical perspective. I hesitate to nod toward descriptive statistics and the frequentist approach as being more or less truthful just because it is more easily accessible for many, as it is generally "front and center" and in abundance owing to its many different potential tests, in many, if not most US curriculums. Also, arguably more pragmatic/efficient from an economic perspective as its allegedly apparently tantamount to an analytical baseline culturally, albeit you'd get no argument from me, that is by no means a guarantee at being ideal across the use-case or potential use-case landscape writ large, and to suggest otherwise would seem to be missing the potential salient implications, which I tend to also agree with any who side with coming out and divulging such implications, as potentially muddiying the waters as to potential personal development, and the joy and importance at having deduced something for oneself. Such as the gaps where dialectics is a hindrance, rather than an aid as to developing a conceptual understanding of some, if not _many_ subjects and topics bearing upon knowledge.

[@mattiasthalen]

I’m curious. Say that I’m in the gym lifting a certain weight. With no more info, I won’t add weight, but if I measure the velocity it could suggest that I could add weight. The decision is binary, add or not add. I’ve thought that this is a good place for Bayesian to shine, letting me know the probability that I could lift x more weight. But your video makes me think that this binary decision leans more toward frequentist thinking. The underlying assumption in my specific example is that there is an inverse relationship between load lifted and the max velocity you can lift it.

[@Artaxo]

What if I went bayesian in the first question and frequentist on the second? On the third, I couldn't even choose. But I 100% subscribed, great video!

[@Antharmo]

Oh, so that's why she called herself a 'recovering' statistician

[@purefatdude2]

I don't get it.

[@RavinderRam]

awesome tutorial..

[@franciscodasilva4652]

The Bayesian perspective reminds me very much of Schrödinger's cat experiment. :-)

[@carlosflorescabanillas7801]

Good explanation

[@shyamkothari596]

what is the 'validity' of the question about probability of heads or tail once it is landed in your palm in this example is not clear ? what are its implications ?

[@bfredl]

Amazing. You explained so much in 7 minutes that I previously only have seen explained with much more time. For myself, my data science problems are tricky enough to need the best methods from both camps :)

[@jesse2667]

This video was amazing. I find myself with both, but lean toward the Baysian to answer this coin toss question from the perspective of "given the information I have available to me, my answer is ___". But based on this description, frequentist favoring more ground truth, then I would think for something like testing of a medical diagnostic machine accuracy, I would not care about only the information I have, but also the information I don't have because the true answer could be more important.

[@mohammadmujeeb6592]

You deserve more views and likes dear

[@mischatal]

Quality and practical use go hand in hand.

[@paulomateus5356]

Congrats Cassie! Your explanation is pretty good. I really appreciate your videos. 👏🏻👏🏻👏🏻

[@raljumah]

do you have to choose one?

[@amante2443]

I believe I cannot love this video enough. I've watched it over and over and recommended it to many people. But then again this is just my belief which I'm willing to change in the future based on the data. I believe this makes me biased towards being a Bayesian, but someone correct me if I'm wrong. 😇

[@DaveScottAggie]

I think I flipped back and forth while listening to you talk about the 2 ways of thinking. I think both ways have merit, but I believe that I lean towards Frequentist.

[@kozyrkov]

Then the last answer in the description is for you, David! :)

[@DaveScottAggie]

Oh wow yes! I was watching on my phone. I found where my mobile KZbin "hides" the descriptions. Thanks.

[@benlacar2109]

Awesome video and great explanation! I came across a problem recently that seems like a mix of both philosophies but I can’t wrap my head around how to solve it! Let me tweak the problem to match your example. Let’s imagine that you made this video twice, in one you used a fair coin and in the other you used a biased coin. However, we, as the audience do not know which is which. How can we distinguish with

[@SunWarrior155]

Forgive my stupid question (which might be slightly off-topic), but doesn't it matter which side is facing up before the flip, how the coin is placed on the thumb, how much force it used for the flip, and some other factors (which may or may not be negligible)?

[@isaacrobertson4374]

Is it possible to be a frequentist before the coin is flipped, like the answer of that coin landing heads or tails is already 100% or 0% which is predetermined by a multitude of factors such as its orientation at the moment, how hard you will flip it and so on, all of which are all also pre-determined but we just don’t know them

[@seriouscoffeecup5516]

How does this apply to quantum superposition?

[@rohanpandey2037]

Interesting paradox (?) I just came across: The probability that I'm a Frequentist is 50%.

[@jorgecelis8459]

so you are bayesian 100%

[@alanbarnett718]

You didn't know what it was before you saw this video. So the probability was 50%. If you're a Frequentist. Now you can't make up your mind, so the probability is still 50%. If you are either. When you've made up your mind, but haven't told anyone, It's still 50%. But only if I'm a Bayesian. I have a pain between my ears.

[@Tout-Le-Monde02]

If the coin is heads-up before tossing, it will always land heads-up, and vice-versa, IF it's landing on the palm of the person tossing the coin. If it's landing on hard surface, it can go anywhere due to the bounce / twist / turn of the coin multiple times before it comes to rest. So, which one am I?

[@foobargorch]

What's the frequentist perspective on getting the model right? It always seemed to be like bayseans try to make that more explicit but I struggle to see a real distinction, but I'm kind of convinced (after adjusting my prior ;-) I just don't understand the semantic distinction that the frequentist perspective had with respect to (implicit) assumptions

[@shivamjalotra7919]

Finally I get the point of difference between them but I am more confused now which of these I am ? How to deal with that ?

[@johnuskglass5428]

As a kid, I used to wonder what the space between the stove and the pot was called. As an adult, I had the same issue with frequentism, I just didn't know how to formulate the question.

[@shenruivah6617]

This, absolutely have no idea why it's in recommendation. Don't know why I watched the whole thing too.

[@andarted]

The algorithm moves in mysterious ways, brother.

[@JWhitty]

because google wants you to further separate yourself from others by now thinking about this

[@FlumenSanctiViti]

In part, it's an ASMR. Especially if your head is inbetween good pair of headsets.

[@dgodiex]

​@@JWhitty Jokes aside, this sounds terribly on point, looking at the current state of affairs.

[@JWhitty]

@@dgodiex of course it's on point. 90% of what is put out by any entity with massive reach is to manipulate opinions. what the fuck is the point of pushing a psychology video to BILLIONS whose only real message is YOURE ALL DIFFERENT AND NOW GO THINK ABOUT IT? She keeps hinting at "what is right? what's more logical? EHHH..." and she leaves it open for people to start mentally pointing fingers. she's egging you on, begging you to judge them. its such bullshit and MATTERS 0%.

[@greglee7708]

great one

[@ssrrapper]

I'm trying to do Bayesian inference where the prior and likelihood distributions are Cauchy distributions. Can you (or anyone else) point me in the direction of how to do that? There are lots of resources online of how to do Bayesian inference with normal distributions and beta and gamma distributions, but I don't see anything involving Cauchy distributions.