Taleb's favorite topic is the "black swan event" which is something that the normal distribution, and the idea of standard deviation, don't model that well. In a normal distribution very extreme events should only happen once in the lifetime of several universes. Of course assuming variation inline with a Gaussian process is at the heart of how the Black-Sholes model calculates risk/volatility/etc.

Benoit Mandelbrot argued that financial markets follow a distribution much more similar to the Cauchy distribution (specifically the Levy distribution) rather than a Gaussian. The problem of course is that the Cauchy distribution is pathological in that it doesn't have a mean or variance, you can calculate similar properties for it (location and scale), but it doesn't obey the central limit theorem so in practice it can be very strange to work with.

The normal distribution is fantastic in that it does appear frequently in nature, is very well behaved, and has been extensively studied. However a great amount of future progress is going to come from wrestling with more challenging distributions, and paying more attention to when assumptions of normality need to be questioned. Of course one of the challenges of this is that the normal distribution is baked into a very large number of our existing statistical tools.

"People rarely check how closely their data conform to the standard distribution; indeed, many people blindly apply the standard deviation to their data regardless of its distribution! The resulting number is often more obfuscatory than helpful, to the extent that it crowded out more useful summaries.

"It's a useful metric when treated carefully, but it is rare to encounter it treated carefully. Science courses would be well-served to stop teaching it in favor of a stronger emphasis on multiple distributions. (Multiple distributions are usually touched upon, but implicitly our curricula overfavor the Gaussian distribution and end up accidentally implicitly convincing students its the only one.)"

But that's just me.

But... it doesn't. You ever hear about the hypothetical possibility of your atoms lining up and falling through the floor?

It's hypothetical in the sense that it's really ridiculously unlikely, but there is no bound preventing it.

Now the central point about different probability curves stands, but that's not what Taleb was talking about--he seems to think that it's the tool's fault if people are using it wrong--and it's also not what Homunculiheaded argued.

A bad example; that's a very, very large sample space, such that deviations from mathematical perfection are irrelevant. They do exist, if you're precise enough (for instance, the universe is not modeled by perfectly continuous space), but I'm not inclined to argue them, because it's too easy to argue that they're irrelevant. So instead consider something more human-sized: Match a normal distribution to the height of human beings.

It works very well, except in real life, the probability of a negative-height human being is zero. This is not what the Gaussian model predicts.

Unfortunately, rather more science takes place in the second domain than the first.

"that's not what Taleb was talking about"

I'm quite aware. The fact that I commented on how I got something other than what I expected rather suggested that, I thought... The fact that this isn't precisely what Homunculiheaded said is also why I posted, rather than just upvoting....

Ah. I misread the following...

>>This is actually what I expected to read:

as agreement ("This is actually what I expected to read."). My mistake.

>Unfortunately, rather more science takes place in the second domain than the first.

As I said to Homunculiheaded, this is because of the relative utility of the models, which we understand--and even those that do not understand it do not make the tool's use invalid.

What are we bemoaning, here, but actual misunderstanding itself?

And really, what's the point of that?

Realization by who? If you understand the normal distribution you had damn well better know that there are other probability distributions.

>The problem of course is that the Cauchy distribution is pathological in that it doesn't have a mean or variance, you can calculate similar properties for it (location and scale), but it doesn't obey the central limit theorem so in practice it can be very strange to work with.

In other words, we're using the normal distribution as the workhorse because considering other distributions is, well, inefficient/unproductive.

> However a great amount of future progress is going to come from wrestling with more challenging distributions, and paying more attention to when assumptions of normality need to be questioned

What exactly is it that you think physicists have been doing for the past half century? The error accounting for CERN's experiments requires actual millions of PhD-hours.

This topic's conversation is at some bizarre intersection of good intentions, concrete knowledge, and woeful ignorance. I guess I tar myself with that brush.

See Glyptodon's post: https://news.ycombinator.com/item?id=7065067

If you hang out with mathematicians, yeah, sure, everybody knows there's a ton of distributions. Try hanging out with, say, biologists. The undergrad statistics education is basically "mumble mumble guassian mumble hideous equations mumble mumble YOU MUST DO THE CHI-SQUARED TEST mumble mumble poisson mumble hideous equations mumble YOU MUST DO THE CHI-SQUARED TEST mumble mumble CHI mumble calculus is hard mumble mumble NULL HYPOTHESIS mumble CHI CHI CHI WE WILL DRUM YOU OUT OF THIS DISCIPLINE IN DISGRACE IF YOU DON'T DO THE CHI-SQUARED TEST".

I'm hardly even exaggerating! I remember being asked by someone in their third semester of using the damn test what it actually meant.

Nominally, yes, the Poisson and probably a couple of others were mentioned, but believe me, the ALL CAPS part of the education does not mention them.

You seem to be having some trouble reading what I'm writing, rather than what you think I should be writing.

I think it's rather silly to talk about "retiring" standard deviation, but we can't blame Taleb - the publication itself posed the question "2014: What Scientific Idea is Ready for Retirement?" to various scientific personalities.

What Taleb failed to mention is that, once properly understood, standard deviation has distribution interpretations that can be much more useful than MAD. For example, if the data is approximately normally distributed, then there is approximately a 99.99% probability that the next data observation will be <= 4 * sigma.

Not everything is approximately normally distributed, but a lot of phenomena ARE normally distributed. It's a well known fact that the phenomena which Taleb is most interested in (namely, financial return time-series) are not normally distributed. But I would like to know how Taleb proposes to "retire" volatility (sigma) from financial theory and replace it with MAD? Standard deviation is so central in finance that even the prices of some financial instruments (options) are quoted in terms of standard deviation (e.g. "That put option is currently selling at 30% vol"). How do we rewrite Black-Scholes option pricing theory and Markowitz portfolio theory in terms of MAD and remove all the sigmas everywhere? Surely Taleb has already written that paper for us so that we can retire standard deviation?

This wouldn't matter if the down-side wasn't so crippling.

I don't think Taleb has to be the one to propose a replacement for portfolio theory, and I think criticism of him for not doing so is pointless. You don't need to have a spare tire handy to point out that your neighbour's car has a flat, and you don't have to run an airline to tell people not to get on a plane with the engines visibly on fire.

B-S vols are putting the "wrong number into the wrong equation to get the right price" as Rebonnato famously said.

Could you sketch out why similar statements couldn't be made about MAD? My (possibly flawed) intuition is that the expected proportion of observations within n\*MAD should be similarly independent of the parameters of the normal distribution.

Squaring error isn't just a convenient way to remove sign, it's driven by a lot of data-sets' conformance to the central limit theorem.

   A stock (or a fund) has an average return of 0%. It moves 
   on average 1% a day in absolute value; the average up move 
   is 1% and the average down move is 1%.

I feel like it is the same kind of failing due to human perception of language that programmers have with the idea of exceptions and errors, especially the phrase "exceptions should only be used for exceptional behaviors". That's a cool phrase, but people latch on to it because of the word exception sounding like something extremely rare and out of the ordinary whereas we see errors as common, but they are in fact the same thing. Broke is broke, it doesn't matter what you call it, but thousands of programmers think differently because of the name we gave it.

We are human and language absolutely plays a role in our perception of things.

Yes! Because it's an awesome trick and lets you do good estimates on napkins.

The other day I was buying lunch at a food cart and thought about how much change the food carts had to carry, as a function of how many customers they have, under the assumption that they want to be able to provide correct change to 99% of their customers.

Let's say that the average amount of change a customer needs is $5, and a 99-th percentile customer needs $15 in change. If we pretend that the distribution is approximately Gaussian we can calculate that 1,000 food carts with 1 customer each would need $15,000 in change, but 1 food cart with 1,000 customers would need $5 x 1,000 + ($15 - $5) * sqrt(1,000) ≈ $5,320. That's math you can do in your head without a calculator (being a programmer, 1,000 ≈ 2^10 so sqrt(1,000) ≈ 2^5).

The standard deviation and assumptions of normality are so useful because of the central limit theorem. That is, if you have many iid variables which have finite standard deviation the sum will converge to a Gaussian distribution as the number of variables increases.

Then you say "Well, the standard deviation weighs the tail too heavily" and the response is "well use higher order moments then, that's what they're they're for".

The quantitative work I do has to do with measuring latency, where the minimum, median, 90%, and 99% values are more meaningful than the mean or standard deviation. Programs typically have a best-case scenario (everything cached) and a long one-sided tail.

But it's good to have bad estimates, at least, it's better to have bad estimates than it is to have no estimates at all. I'm not saying that standard deviation is a substitute for more thorough analysis, just that standard deviation is an improvement over just talking about the mean.

Another example: We'd like to hire you, the mean number of hours per week you'd work is 40.

Versus:

We'd like to hire you, the mean number of hours per week you'd work is 40, and the standard deviation is 15. So your bad estimate is that you'd have two 70-hour weeks each year. But it's better than no estimate.

I hate to quote XKCD, but it's like saying your favorite map projection is a globe (http://xkcd.com/977/). Yes, you've preserved all the data, but even with computers, your beloved graph will not make it all the way to the end.

I'd rather not feed two points to my decision algorithm, whether it's machine learning or a human looking at the data. It makes more sense to make some attempt to preserve the shape of the graph unless you have strong reason to believe it's Gaussian, and even then the assumption should be checked.

He strikes me as someone who is so desperate to be important and recognized that an assertion like this doesn't really surprise me.

"Virtue is when the income you wish to show the tax agency equals what you wish to show your neighbor"

"The problem is that academics really think that nonacademics find them more intelligent than themselves",

etc

I've read black swan as well, and there were parts I didn't quite grok at the time (You cannot predict a black swan! etc)

My take is that he's a Malcolm Gladwell with numbers and therefore less easily takedownable but has the same model of "hold prestigious academic position, appear wise, publish book with simple principle, be easily referenceable by people who want to sound well-read" except he has lots of math and charts to point at lest anyone call him out on it.

Don't try to engage with him critically, but constructively, directly on the internets though, because he prefers acolytes to arguments!

The book does make a good point though.

Maybe it's because I think like a programmer rather than a finance guy, but a large portion of what I do on a daily basis is about mitigating risk from the unknown. A programmer who only guards against known risks is going to get his ass handed to him sooner rather than later (these are the sorts of people who use blacklists and regexes to sanitize SQL). A finance guy who only guards against known risks is just playing the averages. My experience with traders is that they tend to be pretty abstracted from reality and like to impose rules and patterns where there are none. I can see why those sorts of people would find Taleb's work groundbreaking.

Taleb's undoubtedly intelligent, but I feel like he woke up one day, decided that he wanted to be a philosopher who brings wisdom to the masses, and built his temple on a mind-searingly obvious principle, which he now proclaims to be his great gift to humanity, for which he should be praised, hallowed be his name. The impression I got off of him was that he considers himself a prophet, imparting a word to the masses that the rest of us are just too stupid to recognize. Gag me.

It can be difficult for folks (myself included!) to separate their message from their irascible personalities.. ;-)

If none of this makes sense, take a look at the LessWrong site.

Here's Taleb on Mandelbrot: "[He] had perhaps more cumulative influence than any other single scientist in history, with the only close second, Isaac Newton."

That quote really says it all.

More importantly, it doesn't fix the real problem, which is that the mean and standard deviation don't tell you everything you need to know about a data set, but often people like to pretend they do. It's not rare to read a paper in the soft sciences which might have been improved if the authors had reported the skewness, kurtosis, or similar data which could shed light on the phenomenon they're investigating. These latter statistics can reveal, for instance, a bimodal distribution, which could indicate a heterogeneous population of responders and non-responders to a drug, and that's just one example.

I'm not a statistician, so some of this might be a bit off.

Without computers, this would be a waste of paper, but transmitting the data electronically is cheap.

So why argue over the measurements? Publish the data and my software can give me any measurement I'm interested in.

>>The notion of standard deviation has confused hordes of scientists

What an assertion! It also proved to be very useful for hordes of scientists... what about some examples of confused scientists ?

>>There is no scientific reason to use it in statistical investigations in the age of the computer

As someone who uses it daily I am eagerly awaiting his argument.

>>Say someone just asked you to measure the "average daily variations" for the temperature of your town (or for the stock price of a company, or the blood pressure of your uncle) over the past five days. The five changes are: (-23, 7, -3, 20, -1). How do you do it?

Ok... if I am to calculate the average I am calculating the average if I need to know standard deviation I calculate standard deviation...

>> It corresponds to "real life" much better than the first—and to reality.

What the flying fuck. What "real life" ? Standard deviation tells you how volatile measurements are not what mean deviation is. Those are both very real life things just not the same thing.

>>It is all due to a historical accident: in 1893, the great Karl Pearson introduced the term "standard deviation" for what had been known as "root mean square error". The confusion started then: people thought it meant mean deviation.

I don't know how one can read it and not think: "is this guy high or just stupid?".

>>. The confusion started then: people thought it meant mean deviation.

I am yet to see anybody who thinks that standard deviation is mean deviation. It's Taleb though. Baseless assertions insulting groups of people are his craft.

>>What is worse, Goldstein and I found that a high number of data scientists (many with PhDs) also get confused in real life.

One example please ? I can give hundreds when std dev is useful and mean deviation isn't. Anything when you decide what % of yoru bankroll to bet on perceived edge for example.

Ok so he asserted that people should just use mean deviation instead of mean of squares. Guess what though, taking the squares have a purpose: it penalizes big deviations so two situations which have the same mean deviation but one is more stable have different standard deviations. THis information is useful for many things: risk estimation or calculating sample size needed for required confidence (if you need more experiments, how careful should you be with conclusions and predictions etc). He didn't mention how are we going to achieve those with his proposal. Meanwhile he managed to throw insults towards various groups without giving one single example of misuse he describes.

This is not the first time he writes something this way. His whole recent book is like that. It's anti-intellectual bullshit with many words and zero points. He doesn't give any arguments, he throws a lot of insults, he misues words and makes up redundant terms which he then struggles to define. The guy is a vile idiot of the worst kind: ignorant and aggressive. Him gaining so much following by spewing nonsense like this article is for sure fascinating but there is no place for him in any serious debate.

> What an assertion! It also proved to be very useful for hordes of scientists... what about some examples of confused scientists ?

He is exaggerating, for sure. But the point is valid: the mean average deviation (MAD) is often very different than the standard deviation (STD), and the MAD is more intuitive, it has a natural geometrical interpretation - STD's usage of squaring the distance makes it more complex.

And yes, this confuses people in some cases, including scientists. Many scientists are not statistical experts, they use tools as they were taught, and they often assume MAD is approximately the STD, because it usually is, except in rare cases when it is not. I've seen examples of those people in grad school, he is not making this up.

The STD is far more easy to analyze in a mathematical way. That is the huge value it brings - squaring is an operation you can take the derivative of, but absolute value you cannot. STD gives us nice properties like easily provable sum of variances is the variance of the sum, for independent variables.

MAD, however is nicer for reporting data since it is more intuitive. I think he makes a valid point that STD is used more frequently than it should be.

> Ok so he asserted that people should just use mean deviation instead of mean of squares. Guess what though, taking the squares have a purpose: it penalizes big deviations so two situations which have the same mean deviation but one is more stable have different standard deviations.

His point is that many people are not aware of that property and do not want it.

If I ever met anyone in real life who was as impressed by his book as as much as the inexplicably fawning blogs and reviews I have seen, I would give it another try, but I sorta feel like I've been had. At least it was a library book, so I didn't give him 12 bucks.

Anybody here love his stuff, wanna convince me to try again?

Fooled by Randomness[1] was very good. It's more of a set of cautionary tales, mostly pulled from Wall Street, about how to think about chance and making sure you're judging all events against what is expected by chance.

The thing about Taleb is that all of his success hinges on a single, long-ago tail event that few people even remember. It defines his entire outlook and he often assumes that people who interview him or read his articles know this. The article in question not withstanding, if you mentally insert "when discussing tail events" before his claims, many of them make much more sense :)

[1] http://www.amazon.com/Fooled-Randomness-Hidden-Chance-Market...

I actually love Taleb's books (Black Swan, Antifragile). And I work with "data scientists", most with Ph.D.s, many in statistics.

I always talk about his book, but I cannot for the life of me find a single person who's even read it. I think: these are some the ONLY book about stats on the NY Times best seller lists (up until Nate Silver). And yet all these professionals have not only not read it, but barely even heard of it?

My hypothesis was that it is more popular on the East Coast than the West Coast (of the US). Are you from either of those places? I am originally from the east coast but work on the west coast. To hand wave a bit, I feel like west coast people are less into "ideas" and more into actions and experiences. Taleb's ideas do have somewhat of a nytimes-ish new yorker-ish east coast culture flavor. And a lot of people working in Silicon Valley are not really that interested in philosophy.

On the subject of his writing, I can totally see that people can be turned off by his writing. He can be arrogant and insulting. I find it kind of funny, but that's a matter of taste.

A few things I remember from his books that I really liked:

- The story of Nobel prize winner Myron Scholes, and namesake of the Black-Scholes equation, which I learned about in computational finance in college. He started a company "Long Term Capital Management", to monetize this ideas, and promptly lost billions of dollars. http://en.wikipedia.org/wiki/Myron_Scholes

That's not interesting? I think the difference between theory and practice is intensely interesting, and Taleb has a lot to say about it.

- I largely agree with his philosophy that people who claim to know things cause more harm than good. The downfall of Alan Greenspan and Bernanke is their arrogance. They think they can control the economy. But they can't and caused millions of people real harm.

- Respect for the old. For all its virtues, Silicon Valley does have a severe case of "neomania". Taleb's ideas about things that last apply to software too. Unix and C are going to be around a lot longer than say Hadoop or Puppet/Chef.

- The philosophy of fragility also applies to software in a straightforward way. Most people know this now, but you should continually expose your software to users and the market, not build up grand ideas in your head.

- actions over knowledge, i.e. people who know how to do things but not explain them

I could list a half a dozen more important ideas but I'll stop there.

I did write in my comments about this book that he overreached on his "trilogy" idea for the Antifragile. But I do like how he draws together a lot of seemingly disparate ideas that are philosophically related.

FWIW, I'm from the west coast, but have spent the last several years in New York working with natural sciences students and post-docs. I might be what you would call an "ideas" person. Abstraction appeals to me. People in our department seem happier when their work is closer to physical observerations. Measuring stuff with yard sticks and radar: good. Getting big piles of data from other people, and doing stats: OK. Fitting a parematerized model to someone else's data: healthy skepticism. Of course healthy skepticism is generally cultivated.

It wouldn't be surprising if our friends reading lists (and reactions) depend on what they do for living. Did you meet a lot of people in finance or economics on the east coast? Maybe people's professions are spatially correlated.

My peers seem to treat models cautiously (including their own), and have tended to respond to abstract economic ideas with measured skepticism. I can't speak for them, but I sometimes perceive that economic arguments are suspected of being insufficiently empirical and subject to ulterior motives. Which, it seems, is at least a part of what Taleb is complaining about. Obviously these things can be true of any kind of argument, I'm just reporting my impression. Anyhow, it would be easier to listen Taleb if his tone was more restrained.

Most of the economists I have paid attention to (which are not near as many as I would like) seemed inclined to provocation. Sowell in "Basic Economics", and Friedman in his speeches (I haven't read his papers) tend to poke fun at their fellow citizens, for example. I think they sometimes alienate those outside their discipline because of this. It makes reading fun though. I find Friedman very amusing, and I think so does he.

I guess I share Taleb's problems with models. Even before I read Taleb I would say to myself "the map is not the territory", particularly with regard to software abstractions. I think the space between the model and reality is where you find a lot of interesting things (including the ability to make a lot of money).

I also share Taleb's skepticism with economics. The core problem is that it's not really a predictive science. It's a lot of people talking about stuff. Did those ideas help anyone? You can make a good case that they hurt a lot of people. If they are so smart, why aren't they rich? The Scholes case is a great example of that.

I'm currently reading "The Signal and the Noise" by Nate Silver, which is actually a fantastic complement to Taleb's books. They say very much the same things, in very different ways. The good part is that you will not be turned off by Silver's prose -- he's humble and very readable. I didn't follow 538 at all, and didn't pay all that much attention to the 2012 election, but I can tell that his writing skill was a big reason he became so popular.

To give an example, Taleb talks over and over about "negative knowledge" -- what not to do, what things don't work, etc. And Nate Silver says the same thing. To make accurate predictions and models, you have to be aware of known classes of mistakes, cognitive biases, etc. and not fall into those traps. People often think that they need to improve themselves by learning more. But for a reasonably smart people, the bottleneck to your effectiveness is actually thinking that you know something you don't.

I am also an "ideas" person but I share the utilitarianism and empiricism of Taleb. There has to be "skin in the game", as he says. There are so many ideas out there, and generally most philosophical arguments (and journalism, advocacy, etc.) boil down to confused semantics. So the way to find truth is through actions and experiments. Economics fails these tests for truth.

EDIT: Nate Silver talks about this paper: http://www.plosmedicine.org/article/info:doi/10.1371/journal... This would resonate with Taleb quite a bit. Most ideas are false, including published ones. It would actually violate economic theory if that weren't the case -- if most science was true -- because scientists have bad incentives (something I know from direct experience).

It's less intuitive, not more intuitive, at least for me. And the standard deviation definitely has a more geometric interpretation than MAD. If you measure a hundred samples, and you want to figure out how much they differ from the expected values, what could be more intuitive than Euclidean distance? But most people never bother to try and extend their intuition about ℝ^3 to ℝ^100 to realize how simple standard deviation truly is.

What is being advocated here is the use of the L_1 norm (MAD) over the familiar L_2 norm (standard deviation). Everybody knows and understands L_2, and L_2 has a lot of desirable properties.

While the standard deviation is proportional to the L_2 distance betwen the vector of the samples and a equal sized vector with all coordinates as the mean, that's not an intuitive expression based on the problem statement.

That's the danger I'm talking about when you start using the word "intuitive". Intuition is relative, and someone who works with mathematics or statistics will develop a mathematical intuition about things. Just like if you're an experienced driver you'll intuitively know when other drivers are about to change lanes, even before they signal.

I think of L_2 more intuitive because it physical space uses the L_2 norm.

The other thing that makes L_1 counterintuitive is that if you measure the absolute deviation from the mean, then you aren't minimizing the deviation—in order to do that, you have to choose the median.

In other words, you say "this is the center" and "this is the measure of how far away everything is from the center", but you could have picked a different center which has a lower distance from your data. Counterintuitive.

   sqrt(x²+y²+...)

   sqrt(x²) = |x|

If I'm not mistaken, with 3 sample points a, b and c that have an average mu, then

  sigma = |(a, b, c) - (mu, mu, mu)|   (L2 norm in R3)

Which one, the mean average deviation or the median average deviation?

This is also what I have read (I have essentially no experience with statistics).

> squaring is an operation you can take the derivative of, but absolute value you cannot

We can talk without wild, obviously false hyperbole. It's trivial to take the derivative of the absolute value function. Working with it is more difficult, but taking the derivative is as easy as anything in math:

    f'(x) = -1 (when x < 0); 1 (when x > 0)

Consider a variant on the absolute value function:

    f(x) = x (when x >= 0); sin x (when x <= 0)

The tangent function has discontinuities at x = pi/2 and every interval of pi from it. When taking the derivative, is it more fruitful to say "you can't just brush away those discontinuities", or "sec^2 x"? And this is a derivative all calculus students are required to know!

http://xkcd.com/386/

I have a hard time fathoming how anyone could think such a thing. This is math: we should be using things because they map to reality in some way, not because they are aesthetically pleasing.

The standard deviation maps to processes where the "importance" of changes is proportional to their square. For example, electrical power is proportional to the square of voltage, so AC power systems are conventionally rated by standard deviation. My 120 V power outlet has a standard deviation of its potential of 120 volts.

Standard deviation is also commonly used in situations where we cannot put a number on the importance of the deviation but we know it is big.

The mean average deviation is useful for numbers that are directly proportional to their importance. For example, if we have a hundred lamps and we measure their optical power outputs, the MAD would be a useful measure of their variation. Optical power is already in units of oomph.

Sure, but the point of the article is that STD is used very commonly, in places where that does not make sense. For example, it is common to see things like "the weight of the test subjects was 170cm (STD 5cm)".

Because of the central limit theorem, many distributions encountered in science are approximately Gaussian, which is parameterized by its mean and standard deviation. According to Wikipedia: "Height is sexually dimorphic and statistically it is more or less normally distributed, but with heavy tails."

On top of that, we have well-understood and easy to compute estimators for standard deviation. Using the sample variance is not a bad estimator at all, the only real disagreement is whether you divide by N or N-1.

I realize calling people idiots are frown upon here but I am ready to defend it. There isn't one other author which infuriates me with his ignorance and inability to make specific point without throwing condescending remarks. If there is anybody who deserve the term it's him. I mean "we should retire standard deviation because everybody misues it and it's not useful for real life" without any examples ? He just called out thousands of scientist, risk managers and statisticians without giving one single argument. His suggestion to just use mean deviation is laughable and obviously doesn't work for many purposes which he didn't address while being condescending in the process.

It's worse than saying: "we should retire Java and just use Python because it has dynamic typing and seemingly developers at even serious companies fail to see this!". Remark of this kind would gain me "idiot" badge pretty quickly. This article is on this level (well, actually lower as remark about Python actually contains an argument) and his book is worse. If that doesn't qualify him as an idiot I don't know what else could.

You seem exasperated at his condescending attitude, but make no effort to hold higher ground. That said, even dismissing him as a 'crank' would add more substance to your argument than calling him a 'vile idiot.'

I'm not saying you have to play softball, and there are plenty of targets for your rage. Call his condescension vile, and his poorly-thought out conclusions laughable. Instead of saying he's an idiot, you can say that he's being lazy and should know better exactly because he's NOT an idiot.

Calling him a crank is a more objective statement, and to me seems to impute an unknowable motive to what Taleb is doing in these types of articles. Is it an egotistical attempt to feel important, a strategy to sell books, or does he believe all the stuff he says?

What I personally find so unpleasant and objectionable about Taleb is probably unfair, but he seems to fit the archetype of an entire group of people who make loud statements that are wrong or unsupported, but take a long and nuanced discussion to explain why.

For example he seems to be terrible at economic analysis, but portrays himself as an excellent trader so he asserts that scientific study of the economy is bunk. Let's say that he really is an outlier in finance, operating with localized phenomena is different than having a comprehensive view of the economy and refining models.

I think his idea of Black Swans is incredibly useful, and it is interesting to explore the psychology of underestimating the likelihood of rare events. His contributions are less useful when he sounds like the people who say "economics is not a science", before making a bold assertion about the economy that has no rigorous models to support it. Such situations are analogous to saying meteorology is not a science when it comes to forecasting whether or not it will rain in ten days. "A science" is a field where everything is known? "A science" is not a field where models are refined through research and experimentation? The science part of meteorology is the process of understanding the mechanisms behind how air masses interact and improving models, not predicting weather at a future date when a good deal of what will determine whether or not it rains has not yet occurred.

Then you are a vile idiot.

I started this subthread by pointing out that 'bluecalm had weakened his argument by using the words "vile idiot". But 'bluecalm also made a bunch of testable and non-obvious observations in his comment; his writing had value. Whereas you just jumped in to call someone you disagreed with a name, and nothing else; your writing had no value.

Could you stop writing comments like that?

The point I wanted to make was that "vile idiot" is problematic in itself, and by my meta-use of it, I hoped the parent would obviously see why.

How calling public figures "vile idiots" is any better than calling individual HN commenters the same?

>Whereas you just jumped in to call someone you disagreed with a name, and nothing else; your writing had no value.

Did it really seem like a called him a name just for the fun of it? Wasn't the meta-context obvious?

Maybe it is because "vile" doesn't sound like a very extreme word to me—almost like Daffy Duck saying "despicable". I suppose I would have had the same reaction, if instead bluecalm had denigrated him with a word that sounds more serious to me like "worthless".

Neither "crank" or "vile idiot" would add substance to bluecalm's argument. "Vile idiot" isn't even part of the argument, it's part of the conclusion.

I disagree with bluecalm on the "vile idiot" conclusion - I think that he's either vile or an idiot. If he's really communicating how he sees the world, he's an idiot. If he's just cynically trying to come up with another Gladwell/Taleb style thesis that will catch fire with the NYT and TED crowd and bring in buckets of money, then he's vile.

>>True, while humans self-repair, they eventually wear out (hopefully leaving their genes, books, or some other information behind—another discussion). But the phenomenon of aging is misunderstood, largely fraught with mental biases and logical flaws. We observe old people and see them age, so we associate aging with their loss of muscle mass, bone weakness, loss of mental function, taste for Frank Sinatra music, and similar degenerative effects. But these failures to self-repair come largely from maladjustment—either too few stressors or too little time for recovery between them— and maladjustment for this author is the mismatch between one’s design and the structure of the randomness of the environment (what I call more technically its “distributional or statistical properties”). What we observe in “aging” is a combination of maladjustment and senescence, and it appears that the two are separable— senescence might not be avoidable, and should not be avoided (it would contradict the logic of life, as we will see in the next chapter); maladjustment is avoidable. Much of aging comes from a misunderstanding of the effect of comfort—a disease of civilization: make life longer and longer, while people are more and more sick. In a natural environment, people die without aging—or after a very short period of aging. For instance, some markers, such as blood pressure, that tend to worsen over time for moderns do not change over the life of hunter-gatherers until the very end. And this artificial aging comes from stifling internal antifragility.

In which he claims aging is misunderstood and proposes his new theory that it comes from too few stressors or too little recovery. Then he says that "Much of aging comes from a misunderstanding of the effect of comfort - a disease of civilization". Really ? Aging comes from misunderstanding ? This is just random babbling, there is no sense in it. Reasonable people don't write or talk like this and those who do with such conviction as him are called... well, idiots.

>polyglot who writes classical Greek, Arabic, French, English and can do advanced statistical modeling is clearly an idiot right?

If he is in fact a polyglot and in fact can do advanced statistical modelling (the latter I very much doubt, the former I have no idea about) maybe he is not an idiot but some mental illness is taking a toll on him which makes him write and talk like one. The thing is there is no continuity, what he sees as arguments don't even address the point. It's just stream of words without any essence or meaning. I mean again, read the paragraph I quoted.. it's not even cherry picked. There are worse (like the one about depression or academia). The whole book is like that and article from OP just continues the trend.

>>> Aging comes from misunderstanding ? This is just random babbling,

You are trying very hard to not understand. The thought is simple - comfort has side effects (think obesity, bad nutrition, lack of movement, overuse of pharmaceuticals like antibiotics or mood adjusters, etc.) which are not properly appreciated (they are starting to be, but we are still far from proper realization (understanding) of what and how much we pay for it and doing something about it). Not understanding those effects influences behaviors in such ways that people harm themselves. These effects accumulate and contribute to what is called "aging" - you can eat random junk when you are 20 and be fine, but keep doing it till you're 50 and you'll be the best client of your local healthcare facilities for the rest of your life. And so on and so forth. I won't say it is the deepest of observations - actually, it's pretty rapidly becoming a commonplace and sometimes even a fashion - but it definitely not a "random babbling".

I get an impression that you just came to a hard conclusion that Taleb literally writes nonsense and you are hard set on not allowing any sense that is contained in his writing - and can be easily seen - to get to you. Your right of course, but I personally fail to find any utility in such a behavior.

The guy can't help himself. It's like reading a stock ticker (except its Taleb's stream of thought), flashing across with all the different thoughts that don't necessarily correlate with each other. You think there's some relevancy there but its hard to pick it out in the moment.

I will say though, Taleb reader's are surely great Words with Friends or Scrabble players. You just can't help picking up a few new words.

I don't agree with your 'vile idiot' statement but I mostly concur with your thoughts. I hope that you don't let Taleb affect your senescence.

I personally find his books mostly valid critiques of established "truths", probably because I agree with much of it. If that makes me a vile idiot I am fine with that. Each to their own.

I was about to write the exact same thing. It is very hard to take this criticism seriously when it is written that way.

> Funny I find your comments to be doing exactly what they are critiquing.

I am really not doing it. I gave specific examples of what is wrong with his article and in what areas standard deviation works. I am not defending a view that "standard deviation describes reality better" or anything like that. I am saying why his article is bad and in what areas his solution of just using MAD doesn't work. Those are quite specific things. How can you tell I am doing the same thing I am accusing him of ? While I didn't mention any specific fragment in his book I thought it's a useful view to add it as I've spent a lot of time developing it (I've read 3 books of him and listened to many talks). There is limited space in internet forum post and proving my point would require quoting the entire book as I claimed there is barely any paragraph without nonsense or term misuse. Also my claim is easily verifiable: just start reading "Antifragile" and see for yourself. That can't be said about his claims.

> Nassim is not saying to get rid of the concept, but more like saying a lot of people are not using it correctly.

Really? That's the vibe you get from the article ? Let's see:

>it is time to retire it from common use and replace it with the more effective one of mean deviation

>Standard deviation, STD, should be left to mathematicians, physicists and mathematical statisticians deriving limit theorems. There is no scientific reason to use it in statistical investigations in the age of the computer

>as it does more harm than good

>It is all due to a historical accident: in 1893

He is not saying that some people in some situations misuse the concept. He is saying the concept is dangerous and should be disposed of. I get that he is easy to like as he picks on groups not generally liked but let's stick to what he is actually claiming instead of softening it for him. Again, he doesn't give one simple example of a situation which could be handled better by using MAD instead of now used standard deviation.

I don't think it gets much clearer than that.

This is not true, at least for me. When I first read Taleb's ideas, I had no idea who he was and what is his credentials, but his ideas were interesting enough and challenging enough to look him up and find out what else he wrote. Yes, his style is abrasive and sometimes outright combative. So what. His ideas are interesting, that's what matters. I'd rather have an abrasive person who has interesting ideas than a polite one who has nothing.

And speaking of Python vs. Java things, I'm reading those kind of things literally every day, frequently including here on HN. If idiot badges were distributed for each such occasions, there would be a very sizeable majority wearing them. But I am not convinced calling each other idiots is actually adds anything to the discussion.

Sure, he comes across as arrogant, confrontational and uncompromising and he is unapologetic on top of that; yet dismissing the bulk of his writing based on his personality and style of writing is a shame as the recurring theme throughout all his books are worth the time spent reading. To paraphrase: We believe and accept a lot of things without question and let fallacious thinking and cognitive biases lead us into making decisions and actions that do us harm.

Also, his books are that rare type that you can pick up, read a few pages and have set off a train of thinking for the rest of the day.

Hey, Kettle? It's Pot calling. He seems pretty pissed off about what color you are.

http://falkenblog.blogspot.com/2009/03/fama-on-taleb.html

http://falkenblog.blogspot.com/2012/11/taleb-mishandles-frag...

http://falkenblog.blogspot.com/2010/12/nassim-taleb-imitates...

http://falkenblog.blogspot.com/2009/03/review-of-talebs-blac...

Gladwell plays into the comparison about as much as the editors of Cosmopolitan would play into it if Niederhoffer and Taleb had shared the cover of an issue of Cosmo.

I found the second and fourth links very interesting, thanks for the other perspective.

[1] http://www.dimensional.com/famafrench/2009/03/qa-confidence-...

"Nassim Taleb is a vulgar bombastic windbag, and I like him a lot. His books do a good job of explaining some deep, important finance ideas for a general audience. He has helped popularize the notion of "skin in the game". His trolling of economists is also good for some lulz (I particularly enjoyed his coinage of the term "macrobullshitters")."

[0]http://noahpinionblog.blogspot.com/2014/01/of-brains-and-bal...

  |                           ..
  |                         .    .                 
  |    waste      |       .        .       |     stuff
  |     of        |      .          .      |       I
  |     my        |     .            .     |     don't
  |    time       |   .    stuff I'll  .   |     grok
  |               | .        read        . |       
  |(Nassim Taleb) |                        | .       
  |       .       |                        |       . 
  |______________________________________________________
          -3     -2     -1     0     1     2     3
                       standard deviations

He's getting a lot of hate because he's insulted a lot of people while stretching some of his arguments and opinions too far, but his ideas are still better intellectual reading than most of what's available elsewhere.

If you care to read the comments again, you'll notice many of the disagreements with him are on the level of knee-jerk emotional reactions and not well reasoned criticism.

However, it's true that he's turned/turning into some gigantic obnoxious ego whose ideas are beginning to look like wild gambles (speaking more often than he substantiates.) He's definitely intelligent and I would say a genius, but in each book it's like: 10% great ideas, 30% very interesting intuitions, 30% vitriolic anger at people/institutions/ideas, 30% broken ideas/intuitions. But all is sold as the truth (tm).

If you wish to increase the quality of what you read you could use Taleb's own heuristic: when a book has existed for a thousand years it will exist for a thousand more. Time is a great arbiter for importance.

My point is that you either hazard a guess on which of his ideas are powerful, or you wait until the dust settles and somebody else has done this work for you.

I haven't read Nassim Taleb's work either, but comment forums like HN are a poor place to get an idea of the quality of someone's work unless they have clear and substantial criticisms.

You're usually better off getting your reviews elsewhere.

It took me ten seconds to figure out what 'Goldstein and Taleb' refers to: http://www-stat.wharton.upenn.edu/~steele/Courses/434/434Con...

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=970480

He specifies that SD is reasonable as a mathematical tool, but not for inference about society, finance, etc.

Your comment appears to have an agenda or ideology underlying it.

Further you don't seem to have read the article fully.

>Ok so he asserted that people should just use mean deviation instead of mean of squares. Guess what though, taking the squares have a purpose: it penalizes big deviations so two situations which have the same mean deviation but one is more stable have different standard deviations. THis information is useful for many things: risk estimation or calculating sample size needed for required confidence (if you need more experiments, how careful should you be with conclusions and predictions etc). He didn't mention how are we going to achieve those with his proposal. Meanwhile he managed to throw insults towards various groups without giving one single example of misuse he describes.

That is pretty much what his entire article is about: taking squares may not be the best idea universally.

Ummm, are we both discussing the same article?

> Yes it's ideology of calling people out when they assert incorrect things in condescending tone without any arguments while insulting whole groups of scientists and statisticians.

No. It is the ideology of dissing Taleb. Obviously I am not his paid spokesperson. But please argue to the merit of his arguments.

To quote from Taleb's edge.org article:

>> 1) MAD is more accurate in sample measurements, and less volatile than STD since it is a natural weight whereas standard deviation uses the observation itself as its own weight, imparting large weights to large observations, thus overweighing tail events.

Also he alludes to his paper with Goldstein. It is clear form Goldstein and Taleb's manuscript that that Taleb is not just throwing these arguments to talk trash about practitioners of statistics. They report findings of an experiment with multiple groups of applied statisticians. I'll quote from it [1]:

"We first posed this question to 97 portfolio managers, assistant portfolio managers, and analysts employed by investment management companies who were taking part in a professional seminar. The second group of participants comprised 13 Ivy League graduate students preparing for a career in financial engineering. The third group consisted of 16 investment professionals working for a major bank. The question was presented in writing and explained verbally to make sure definitions were clear."

That makes Taleb's edge.org claims far from unsubstantiated.

[1] Goldstein, Daniel G. and Taleb, Nassim Nicholas, We Don't Quite Know What We are Talking About When We Talk About Volatility (March 28, 2007). Journal of Portfolio Management, Vol. 33, No. 4, 2007. Available at SSRN: http://ssrn.com/abstract=970480

Yes. He doesn't mention the fact that MAD loses information about volatility. He ignores the fact because that would make his whole article look silly.

>But please argue to the merit of his arguments.

It's kinda difficult when he is not saying anything specific. When he says: "look, those scientists don't grok basic math but fail for catchy names instead" without any facts, examples etc. all I can do is call him out on this nonsense. When he argues for abandoning SD I can give situations where it's not gonna work and I did.

>1) MAD is more accurate in sample measurements, and less volatile than STD since it is a natural weight whereas standard deviation uses the observation itself as its own weight, imparting large weights to large observations, thus overweighing tail events.

But this is nonsense. It's like saying measuring temperature is better than measuring mass. Those are just different things to measure and saying one is less volatile isn't really meaningful. SD contains information about volatility, MAD doesn't. That the reason SD is used for many things. When you want to substitute one with the other you gotta address how you handle that lost information.

>Also he alludes to his paper with Goldstein. It is clear form Goldstein and Taleb's manuscript that that Taleb is not just throwing these arguments to talk trash about practitioners of statistics. They

This paper wouldn't pass peer review. What was the methodology ? How much time they have ? Did they have access to a computer ? Even if it was really serious experiment it's just toy example of people getting question asked in tricky way wrong. What about actual mistakes they make in real world because of using SD instead of MAD ? This is what he claims is a problem. He claims SD should be abandoned in favor of MAD, what is one situation which people would get better if they do it ? I am mocking him because is a master of using many words without making a point and somehow is good at seducing readers to like him.

But to just say that some tool is both useful and misusable is boring and wouldn't cause people to talk about this nearly as much.

I don't really like Taleb's style, but I can't deny that it causes conversation about things that people often would not give a second thought. In that way, I can see the outlines of a really great point underlying his inflammatory rhetoric.

Don't hate the player? At least he's arguing that people ought to be smarter and worry about what data really means.

I have found his books more stimulating than Hacker News for example...

Are you still talking about the inflamatory style, or is that about the contents of the article?

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=970480

And I think you should pay more attention to this:

"...as it does more harm than good—particularly with the growing class of people in social science mechanistically applying statistical tools to scientific problems..."

if you want to understand where he is coming from.

The question they asked reads like a trick question to me. And coming up with the right answer would have required working backwards through statistical formula It's the type of thing you do an "Introduction to Statistics" class and then don't think about too often because it doesn't have a ton of practical applications. It seems a lot easier to just ballpark it.

But like with Wolfram I can understand why his way of formulating himself might get in the way of what he is saying.

Well, for sure there are problem with people applying tools without fully understanding them. It's not very interesting statement. As obviously many things in nature form normal distribution and standard deviation describes those distributions well while mean deviation loses information about volatility. If you are going to propose abandoning of standard deviation it would be nice to give some alternative ways for handling those distributions and maybe show how using mean deviation in place where "many scientists" these days use standard deviation improve things. His study shows that people weren't able to answer his question. That could be for various reasons, like reading it too fast. It doesn't really matter. What matter are example of decisions/measurement which could be better handled by "abandoning standard deviation" or maybe showing that those people actually make financial mistakes due to not understanding the difference.

Even if he is right that many traders misuse or don't understand the term it's a problem with education and not with tools being wrong. "We should abandon standard deviation" is wider claim and to defend it you need to show how problems currently handled by using the measure should be handled in better way.

It is however important if large groups of disciplines are not able to apply it properly.

Instead of calling him a vile idiot and refusing to even look at what he is trying to say you could see this statement as a cry for help, a provocation to start a debate and so on.

The deeper issues here, I think is that he is probably not death wrong nor death right and so it's really impossible to prove anything objectively but rather different experiences yield different opinions.

I personally believe that sometimes you have to simplify in order to get a fundamental discussion started. In fact i find the aftermath of those provocations to be the most enlightening.

I am saying that pointing out that some people misuse some tools is not interesting without specific examples and problems it causes.

>It is however important if large groups of disciplines are not able to apply it properly.

Of course it is, let's try to identify such situations... o just let's try to find one problematic example.

>It might not be interesting to you and I am sure you know how to apply them. It is however important if large groups of disciplines are not able to apply it properly. Instead of calling him a vile idiot and refusing to even look at what he is trying to say you could see this statement as a cry for help, a provocation to start a debate and so on.

I've read his 3 books and listened to many talks. He just doesn't have anything specific to say. If he is crying for help as you said why not describe some situations where using standard deviation instead of MAD causes problems ? I am not refusing to listen to him. The fact that he writes about those problem is the reason I invested a lot of time reading his books despite terrible writing and lack of editing. I was willing to go through just to see what he has to say. It turns out there is nothing, not one example of systematic error people make which could be corrected. It's just hate for anything and everything often paired with failure to understand the most basic statistical concept.

>The deeper issues here, I think is that he is probably not death wrong nor death right and so it's really impossible to prove anything objectively but rather different experiences yield different opinions.

He is dead wrong about standard deviation not being useful for "real life". He is probably right about "some people misuses it some of the time thing" but it's really not interesting unless you give something specific which he never does.

>I personally believe that sometimes you have to simplify in order to get a fundamental discussion started.

I agree. The problem is not simplifying he just doesn't have anything to say. It's not one article without examples or arguments. His whole book is like that and then his talks.

> In fact i find the aftermath of those provocations to be the most enlightening.

And I see danger in it. The guy has some serious following. It can't be good if people read his stuff and start believing that those risk managers are just morons, that scientists misuse standard deviation because of misunderstanding in 1893 and all other ridiculous things he claims. You can really gain an impression that everybody is a moron, academia is a waste and math doesn't apply if you don't know better and take his writing seriously.

And you are not the only one who read his books. But again. His criticism aligns with my experience, so what do I know.

Your comment is a bit irritating. I don't know the author of this blog post but I think he has a couple of valid points. You seem to dislike him for some other reasons. Feel free to share them. The blog post is still perfectly reasonable.

Anyway, I agree some of your comments as well, namely this:

> I can give hundreds when std dev is useful and mean deviation isn't. Anything when you decide what % of yoru bankroll to bet on perceived edge for example.

and this:

> Guess what though, taking the squares have a purpose: it penalizes big deviations so two situations which have the same mean deviation but one is more stable have different standard deviations.

But I don't see why this comment from N.T. is stupid:

>>>It is all due to a historical accident: in 1893, the great Karl Pearson introduced the term "standard deviation" for what had been known as "root mean square error". The confusion started then: people thought it meant mean deviation.

I actually agree that standard deviation is a confusing name, why do you think that's a stupid comment?

Really ? You think call for substituting one of the most common use measures in science, finance, gambling etc. with some other measure which doesn't have the same crucial properties without giving any examples of one going wrong and the other being better is reasonable ? He also threw some assertions about general confusion and scientist using it because of misunderstanding in 1893. Again without any example while giving condescending analogy of someone asking you to calculating average and realizing it's not standard deviation. It's not reasonable by any standard. The reason I have very low opinion about his writing and him are his books, especially Antifragile.

>I actually agree that standard deviation is a confusing name, why do you think that's a stupid comment?

He claimed that "the confusion", things he described in previous paragraphs about scientists, PhD's and statisticians is caused by misleading name. I am not saying the name is good but implying that so many people are using it just because the name given to it in 1893 is insulting. It's basically Taleb telling scientists: "hey guys, you use this standard deviation thing because of randomish reason and not because you know what you are doing". It's very strong bold statement which undermines credibility of whole groups of scientists which he again didn't even begin to argue for. He just asserts it and continue.

Because he makes a lot of bold/insulting claims and then fails to give one argument most of the time. When he does provide an argument it's very often nonsense (like with his analogy of someone being asked to calculate MAD).

>Should you not be far more concerned if it's true?

I am. I am also concerned about discrediting scientists without providing reasons. Creationism, anti-vaccination and climate change denial are all based on people not trusting scientists and following guys who just tell them scienc is nonsense. Taleb is very close to that line.

> What does it say about you that you are posting angry vitriolic comments online because someone called out scientists misusing statistical tools but that you are not as alarmed that such a thing could be happening?

He didn't call out anybody. He just threw plain insults without reasons. When you call out someone you say what is wrong with what they are doing, like this: "Hey, bankers, it's unfair that you make huge bets and don't pay out when you lose!", "Hey, investing firms, it's stupid that you treat a lucky few as celebrities while you can't have any confidence in their performance". Here is didn't called out people. He just asserted they are using one of the most used mathematical tools incorrectly, that it's because confusion in naming and that it should be disposed of. He didn't give one example of mistakes happening because of it. Now, people who follow him and believe that he is some kind of authority may start distrusting science or academia (as he is actively hostile vs both) exactly the same way anti-vaccination people do: just because some celebrity told them so. There is value in calling people like him out on their nonsense.

I'm not sure why it took you so long to flip the bozo bit (if you were attempting to dance asymptotically along the edge it really has been a bravura performance!), but that did it.

Alright...

It is ironic that you take Taleb to task for not providing an example, and then you do exactly the same thing that you are excoriating him for. Claiming that you have hundreds of examples is not the same as actually presenting one.

And then the critique of "standard deviation" is that people got taught SD as a statistical tool that you just pull out any time you feel like it, and they don't know what it means in underlying probability-theoretic terms as an assumption about either the world or our own uncertainty, and so it's misused horribly on all sorts of occasions.

I'd guess that SDs are appropriate 40-90% of the time depending on which field you work in, but without a lot of Bayesian background with fairly advanced math people will not be able to really appreciate what the other 60-10% of times look like. And the state of education is not anywhere near like that. It's just people being taught to calculate the standard deviation cause, like, that's something you do. They don't know what assumptions it corresponds to even in the cases where SD does apply.

Burning SDs to the ground and starting over would not be very much amiss in one of the fields where SDs only make sense 40% of the time, but the practitioners are using them all the time. (Machine learning is one of the fields where SDs make sense maybe 40% of the time, and if I found an ML practitioner who had been taught to think in terms of squared error I'd send them off to learn the underlying probability theory until their entire worldview had been translated into likelihood functions instead.)

I think it is still unexplained why the financial community is sticking to things that are known to be broken. It is like trying to use newtonian mechanics to describe chaotic phenomena. Standard deviation does not properly describe fractal stuff.

Feels like the industry is looking for the lost set of keys on a dark street, not where they were last seen but rather under the lamptpost, cause that is where the light is.

I am not saying that the arguments are without merit, just to be cautious in forming an opinion on the man.

On the positive side, if you're going to judge the guy by the insights of his critics, his supporters have included the likes of Benoit Mandelbrot and Jack Bogle.

edit: apparently MAD can refer to either "mean absolute deviation" or "median absolute deviation". Yup, this sure isn't going to confuse anybody.

>> As someone who uses it daily I am eagerly awaiting his argument.

Compute-intensive statistical tests, which are data-driven and make fewer assumptions about the underlying distribution, can give tighter confidence intervals and detect statistical significance better.

For example, stratified shuffling.

There is relatively simple, yet illustrative problem described on the first page. The solution is on the second page. Try to solve it w/o looking the solution.

Why would you use sd to convey the amount of volatility? Isn't the mean deviation much more easily understood?

http://managed-futures-blog.attaincapital.com/wp-content/upl...

A distribution built from MAD will more closely resemble the reality of the S&P returns, and will create more robust models. There are many examples of this.

> As someone who uses it daily I am eagerly awaiting his argument.

The use of root mean square error|deviation comes from the ease of calculation in the days before computers. Now that we have computers we can just use mean absolute error|deviation. Before computers, we needed calculus to find the "best fit line" -- parameters that create the least distance between predicted and actual values. Calculus plays well with parabolas but not so great with absolute value functions. Now that computers are around, we don't need calculus (as much).

But I think the headline was sensationalised.

Taleb doesn't seem to be against "standard deviation" mostly, but calling it "standard deviation" (which should be called something else), and providing a more "user friendly" number.

The positive square root of the second central moment of the density distribution function will keep being that.

Some other points - std.dev shows up in nature and is easier to understand, and analyze, simply because variances from independent sources add up. That's why something called ANOVA exists.

In OLS regression, we minimize the std. dev. of the error, not the MAD. (It is not because statisticians "thought" std. dev is actually MAD, it's because minimizing std. dev or square error has very nice properties that we use it even though the solution to MAD minimization is also known.)

Lastly, I don't follow who he proposes to be using MAD - if he decrees statisticians and physicists should still use Std. Dev?

Often it is that I find these articles, and I let them pass by. But sometimes, they have a potential to come back - and I stand a chance to be quoted this article, by someone else, who I then have to drain my energy to make the point that it's not really worth reporting MAD from now on.

Correct me if I am wrong, but I thought that the term inverse square law was erroneous, and I have yet to come across a complaint from anyone.

"What an assertion! It also proved to be very useful for hordes of scientists... what about some examples of confused scientists?"

How about every single economist and financial analyst who failed to predict the financial crashes of the last 50 years? Did you realize how much money was lost over the last 50 years by the financial industry's reliance on models that improperly use the standard Gaussian bell curve? Taleb pointed out why the Gaussian is not usually an appropriate model for financial analysis and suggested a return to more conservative models. Yet today Modern Portfolio Theory and the Black-Scholes models (which use the Gaussian) dominate in financial schools and institutions despite the fact that they absolutely utterly failed, not badly, but catastrophically in every financial crisis when they were most needed.

You state that "Standard deviation tells you how volatile measurements are". But Taleb shows how financial markets are non-Gaussian and have "fat tails" and gives the data and the supporting arguments.

bluecalm says:

"As someone who uses it daily I am eagerly awaiting his argument."

You're late! Taleb began publishing his arguments years ago and continues to publish. You're way behind. Read his papers and read his books in the following order:

Fooled by Randomness http://www.amazon.com/Fooled-Randomness-Hidden-Chance-Market...

The Black Swan http://www.amazon.com/The-Black-Swan-Improbable-Robustness/d...

Antifragile, http://www.amazon.com/Antifragile-Things-That-Gain-Disorder/...

BTW simply because someone's writing style is different does not mean that he is wrong. To me, complaining about a writing style is a form of ad hominem argument. People are different and Taleb is one-of-a-kind. Initially I didn't see where he was going, but once I realized that he was presenting ideas that were absolutely, utterly novel and had real explanatory power I sat up and paid attention. If you read for the facts and the valid arguments you will see that Taleb delivers the goods.

While I am not a Taleb fan (he had his own black swan and became exactly the reviled expert his own books warn us about), your hostility in this case seems out of place.

The root of his seemingly casual contemplation is that standard deviation is simply misnamed. That this misnaming causes a cognitive dissonance that confuses even knowledgeable practitioners to mentally conflate it with the mean deviation. Which -- as someone in the financial industry -- I can absolutely confirm. It seems like such a minor thing, but the name has tremendous influence on how we parse these things, and the shortcuts we take in understanding things.

>> Which -- as someone in the financial industry -- I can absolutely confirm.

This is what he is doing: he takes common sentiment often and makes into war. "Abandon standard deviation", "the confusion started", "PhD's are confused" It's easy to like him and nod in agreement while omitting that he took it way too far, proposed things way too radical and again: didn't give one single example of stuff not working because of it.

http://en.wikipedia.org/wiki/Black_Monday_(1987)

That unpredicted event made him and his organization loads of money, and made him a sudden seer of markets. Which is humorous because his book Fooled By Randomness is primarily a bit of a bitter tirade about how we declare people savants because they happened to be in the right place at the right time.

Since then he gets the classic seer type treatment, for instance the other post notes that he "predicted" the financial collapse of 2008 (weird that someone would register just to post that). Yet almost everyone predicted 2008. No, seriously, the house bubble and crisis, and the probable impact on the market, was absolutely common knowledge by the time Taleb made his pronouncements, and was the endless material of virtually every financial discussion. There was absolutely nothing of worth predicted in that, but that's what you get when you got lucky once.

I am not sure what would convince you otherwise though, since it seems like you have made up your mind already. Please loosen up on the snark.

And yes, I've seen a lot of claims of various other successes of Taleb, with a lack of any actual citations or proof at all: His own fund folded after yielding mediocre returns, and then he started consulting for a fund that seems to demonstrate a tremendous amount of bluster, with some curious and cringe-inducing ways of reporting returns, the narrative reading like the story of a gambler, telling you with great pride about the 35:1 payout on their roulette bets, skipping over the 0:37 payout on all their other bets.

I've worked with a large number of hedge funds, many of whom made a lot of money in 2008. You haven't heard a word from them because they have no need to do PR or to pitch the one bet they made right. I become suspicious of any who does.

And the danger of Taleb's lucky bet was that he is listened to as an expert, at least by some. Virtually everything he had to say about government intervention after the financial crash has been proven fundamentally wrong -- actual reality demonstrated that. Thankfully a lot of people didn't listen to him.

For instance if you asked 1000 people to guess coin flips, on average one half will be wrong on each flip. So on the first flip you'd drop to 500, then 250, then 125, then 65, then 32, then 16, then 8, then 4, then 2, then you'd -- in a perfectly ideal scenario -- have one person left. A person that remarkably, "against all odds", guessed 10 coin flips in a row right! Surely they must be some sort of magician or seer, right? Yet their probability of guessing the next coin flip is no better than the people eliminated in the first round.

But that is exactly what we do with things like exceptional events: We find the person who was right about a set of events, among a collection of people guessing almost everything, and assume they've cracked the code. Even outside of financial situations (like being a guy who happened to have a portfolio that did great during an extreme black swan on October 19th, 1987, even if that same portfolio generally did terribly), just look at what happened with 9/11: Of all of the millions of scenarios that people concocted for fiction or just postulating, anyone who talked about a plane hitting the WTC suddenly became prophetic.

It's actually a pretty good book, as an aside.

For instance, Taleb's example of fund managers that beat the market for five years in a row. You start with a cohort of 10,000 and a 50% chance of beating the market each year. There will be a group of 300 or so fund managers that consistently beat the market 5 years in a row. This does not differentiate the lucky from the skillful. 1) Analyzing other cohorts that had higher success rates and 2) their investment strategies and decisions would be necessary to make the determination.

Well, a broken clock is right twice a day. Someone who always calls for a market correction will eventually be right. The real question is how will the investment strategy fair over decades or longer.

We may have different takeaways from the book. My takeaway was that one should invest in becoming skilled (learn strategies that work in the long run) rather than seeking or worshiping those who may just be lucky.

The implication is obvious. 'gggggggggggggg is either an employee of yours, or is in some other situation such that she doesn't want to get on your shit list. Nevertheless, the truth must come out! Hence, anonymity.

>>> There was absolutely nothing of worth predicted in that,

I think it was quite worthy for those who was on the right side of the market. From what it looks like, though, a lot of people were on the wrong side. So presenting it as "everybody knew" is a bit misleading, it seems.

There was zero specificity in Taleb's proclamations. It was just the vague "there are a lot of debts and an inflated housing market that is bound for a correction and is immensely interest rate dependent". Yeah, that's great, but is the same thing everyone else was saying.

GM went bankrupt during the financial crunch. This may blow your mind but the bankruptcy of GM was predestined for years -- their debts kept growing larger while their profits stayed static or shrank.

Everyone knew it was coming, it was only a question of when and what would precipitate it. Yet, people still traded in GM. Exactly the same concept -- people don't trade on what they think will happen tomorrow or next year, they trade on what they think other people think will happen tomorrow or next year.

So there is no misleading, and honestly only "rubes" fall for the "I predicted this" bit.

You realize "everyone" and "every economic fear monger" are very distinct groups? Before the crisis struck, those fear mongers were generally thought of as curiosities or cranks, not visionaries. It wasn't that long ago, too early yet to rewrite history.

Moreover, theories like these: http://www.wired.com/wired/archive/7.09/zeros.html http://bullnotbull.com/archive/predictions-2007.html http://useconomy.about.com/b/2007/01/03/2007-forecast-for-th... http://knowledge.wpcarey.asu.edu/article.cfm?articleid=1344 or even this one: http://www.amazon.com/gp/product/0385514344

were pretty popular. At the last one, we've got excellent review from top people at Federal Reserve and Fannie Mae.

>>> There was zero specificity in Taleb's proclamations.

Huge surprise from a guy who talks about principally unpredictable events as the basis of his philosophy. You expect him to talk about black swans and then say "the market would go down X points at day Y"?

>>> In the same way that the world is pretty certain we're going to run out of oil.

Are you sure? http://cnsnews.com/news/article/gao-recoverable-oil-colorado...

A different discussion concerns the fact that Taleb's possible luck in 1987 makes him a prescient seer for things like the financial crisis. But he isn't, and a tremendous number of things he has said have simply been wrong (most notably, and importantly, everything he said about government intervention. He was 100% wrong in every way).

People ask why people get the heckles up about Taleb, and it isn't actually about Taleb at all. It's about the, for lack of a better phrase, Taleb "fan boys". Bizarre that such a thing exists, but people like having their prophet.

Eventually someone (or something) did explain it, but once I understood it, it became clear that it wasn't always a sensible thing to be asked to calculate but was instead just an instinctive requirement.

Taleb is definitely mad but his use of the MAD acronym (mean absolute deviation) is actually correct. However the STD acronym (all caps) refers to "sexually transmitted disease" and not generally used for "standard deviation". Most people use SD, Stdev, StDev or sigma.

Once again his ability to coin new terminology outstrips his ability to form coherent ideas that are anything more than trivial (eg. we have known about fat tails in stock returns for 50+ years). Like George Soros[1], Taleb's success says more about the state of the world of finance than their contributions to our knowledge.

[1]-See his book "The Alchemy of Finance"

The reasoning for this is that many sexually transmitted infections can be acquired, passed on to others, etc. without causing any clinical symptoms. See: HPV, among others.

http://www.fooledbyrandomness.com/FatTails.html

There might be something there for the more rabid critics. At least it will keep them off the internet for a few days...

Please understand that NNT's biggest issues are not so much with the way statistical models are applied to economics and finance, but how social scientists sometimes feel compelled to apply them to social fields as well, which is plain unscientific, dumb, and mostly disastrous.

So when you bear down on his arguments, please keep this context in mind.

Also, the assertion in your post that the misapplication of statistical models in social science is "disastrous" but somehow giving finance a pass? You've got to be kidding me.

Say someone just asked you to measure the area of a circle with radius pi. The area is exactly 31. But how do you do it?

scala> math.round(math.Pi * math.Pi * math.Pi).toInt

res1: Int = 31

Do you pack the circle with n people, count them up and verify n == 31 ? Or do you pour a red liquid into the circle and fill it up, then drain it and measure the amount of red ? For there are serious differences between the two methods.

If instead, you were asked to measure the circumference of a circle with radius pi.

scala> math.round(2 * math.Pi * math.Pi).toInt

res2: Int = 20

You just ask an able-bodied man, perhaps an unemployed migrant, to walk around this circle while another man, an upstanding Stanford sophomore, starts walking from Stanford to meet his maker, I mean VC, well its the same thing...

So by the time the migrant finishes walking around the circle, our upstanding Stanford entrepreneur is greeting the VC on the tarmac of the San Francisco International Airport. This leads one to rightfully believe that the circumference of the circle of radius pi is exactly the distance from Stanford to the SF Airport ie. 20 miles. It corresponds to "real life" much better than the first—and to reality. In fact, whenever people make decisions after being supplied with the area, they act as if it were the distance from their university to the airport.

It is all due to a historical accident: in 250BC, the Greek mathematician Archimedes introduced Prop 2, the Prevention of Farm Cruelty Act ( http://en.wikipedia.org/wiki/California_Proposition_2_(2008) ). No I believe this was a different Prop 2. This Prop 2 states that the area of a circle is to the square on its diameter as 11 to 14 (http://en.wikipedia.org/wiki/Measurement_of_a_Circle ) .The confusion started then: people thought it meant areas had to do with being cruel to farm animals. But it is not just journalists who fall for the mistake: I recall seeing official documents from the department of data scientists, which found that a high number of data scientists (many with PhDs) also get confused in real life.

It all comes from bad terminology for something non-intuitive. Despite this confusion, Archimedes persisted in the folly by drawing circles in the sand, an infantile persuasion, surely. When the Romans waged war, Archimedes was still computing the area of the circle. The Roman soldier asked him to step outside, but Archimedes exclaimed "Do not disturb my circles!" (http://en.wikipedia.org/wiki/Noli_turbare_circulos_meos)

He was rightfully executed by the soldier for this grievous offense. It is sad that such a minor mathematician can lead to so much confusion: our scientific tools are way too far ahead of our casual intuitions, which starts to be a problem with a mad Greek. So I close with a statement by famed rapper Sir Joey Bada$$, extolling the virtues of the circumference: "So I keep my circumference of deep fried friends like dumplings, But fuck that nigga we munching, we hungry." (http://rapgenius.com/1931938/Joey-bada-hilary-swank/So-i-kee...)

Is MAD any better? Definitely. But I'd like to see a visual demonstration of how well it models exponential-based distributions. How well does it describe their "shape", the skew of the tail?

Boy, is that statement useless without any kind of context, example or citation.

- Claims it is useless therewithout.

"But it is not just journalists who fall for the mistake: I recall seeing official documents from the department of commerce and the Federal Reserve partaking of the conflation, even regulators in statements on market volatility. What is worse, Goldstein and I found that a high number of data scientists (many with PhDs) also get confused in real life."

It doesn't tell us what happened, it just asserts that it did in certain contexts. It doesn't cite the paper he presumably wrote with Goldstein (which Goldstein?) about it. I feel like I'm getting a summary of an abstract with all the citations missing.

From ClementM above, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=970480

From that paper:

"We first posed this question to 97 portfolio managers, assistant portfolio managers, and analysts employed by investment management companies who were taking part in a professional seminar. The second group of participants comprised 13 Ivy League graduate students preparing for a career in financial engineering. The third group consisted of 16 investment professionals working for a major bank."

From the article:

"What is worse, Goldstein and I found that a high number of data scientists (many with PhDs) also get confused in real life."

I get that "data scientist" is a really broad term at this point, but I don't think it's a very good description of the people quizzed in this paper, if this is the paper he was indeed referring to.

Cort J. Willmott, Kenji Matsuuraa, Scott M. Robeson. Ambiguities inherent in sums-of-squares-based error statistics. Atmospheric Environment 43 (2009) 749–752.

URL: http://climate.geog.udel.edu/~climate/publication_html/Pdf/W...

We Bayesians have similar notions, but we usually try not to overly bully frequentist methods, the poor things. Also, being familiar with Bayesian methods, a lot of what Taleb is saying sounds vaguely familiar...

Perhaps the Bayesian methods will take the blame in the next financial crisis. Such as the error in estimating a non-stationary distribution and quantifying the uncertainty.

It's just that traders and bankers would fire statisticians who were too vocal about it as wasting their time with unnecessary explanations...

Bayesian methods in general can only take the blame if you can prove some other method being more reliable.

Also they have another advantage: Bayesian methods are so mind blowing and beautiful, that it is hard to blame them for anything!

For example, if X has a standard deviation of s, and Y has a standard deviation of t, then the standard deviation of X + Y is sqrt(s^2 + t^2). There is a geometry of statistics, and the standard deviation is the fundamental measure of length.

To retire the standard deviation is to ignore the wonderful geometry inherent in statistics. Covariance is one of the most important concepts in statistics, and it is a shame to hide it from those who use statistics.

Additionally, I will mention that we do not need normal distributions to make special the idea of standard deviations. In fact, it is the geometry of probability - the fact that independent random variables have standard deviations which "point" in orthogonal directions - which causes the normal distribution to be the resulting distribution of the central limit theorem.

In this case what matters in the end is the kind of impact deviation from mean has on the real world variable you have. I agree that in most Gaussian experiments MAD might be more useful than STD.

STD is more useful when the real world impact of the deviation increases exponentially with the magnitude of deviation and hence it is a good idea of magnify the (x-n) by squaring it. In many cases the impact is linear where MAD clearly works better. For example in cricket where n runs are n times better than 1 run. But in case of shooting. Hitting 9 targets out of 10 might be 100 times better than 1 out of 10 so there MAD will be misleading.

But no, it is just advocating using Mean absolute distance instead of the standard deviation. Which I guess is to be expected from someone whose work focuses mostly on long-tailed distributions.

Still, I think that non-parametric methods are much more valuable as a solution to dealing with non-normal data than what Taleb is proposing.

1) Refer to the analysis of Root Mean Square Error always by that name. (RMS is already often used in certain jargon instead of stddev).

2) Stop treating RMS as a default measure of variance. Treat Mean Absolute Deviation as the default measure of variance, because the figure it provides is more consistent with people's psychological interpretation.

It's not really retiring RMS, just retiring the idea that it is a good default statistical analysis.

Also, yes, his writing style is grating and he takes opportunistic character swipes at pretty much everyone.

Look at an reputable news site or paper. Odds are they post articles based on polls several times a day. How many report confidence intervals or anything of the sort? These are crucial for interpreting polls, but are left out more often than not. Worse yet, many stories make a big deal about a "huge" shift in support for some political policy, party or figure, when the previous month's figure is actually well within the confidence interval of the current month's poll!

Standard deviation, confidence intervals, etc. are all ways of expressing uncertainty, and it's become abundantly clear that the average journalist, to say nothing of the average person, has no clue about what the concept means. If the goal is to communicate with the public, then we really need to take a step back and appreciate the stupendously colossal wall of ignorance we're about to butt our heads against. When we talk about the general public, we should keep in mind that rather a lot of people know so little about the scientific method that they interpret the impossibility of proving theories as justification for giving religious fables equal footing in schools. This kind of ignorance isn't a nasty undercurrent lurking in the shadows. It's running the show, as evidenced by many state laws in the U.S.! There is absolutely no hope of explaining uncertainty to most of these people.

There is hope of explaining basic statistics to journalists, if only because they are relatively few in number and it's a fundamental part of their job to understand what they are reporting. Yes, I just said that every journalist who has reported a poll result, scientific figure, etc. without the associated uncertainty has failed to adequately perform their job. We need to make journalists understand why they are failing. If simplifying the way we report uncertainties will assist with this, then I'm all for it. Bad journalism is a root cause of a great deal of ignorance, but it's not an insurmountable task to fix it.

If you are a scientist who speaks to journalists about your work, make sure they include uncertainties. If you are an editor, slap your peons silly if they write a sensationalistic poll piece when the uncertainties say it's all a bunch of hot air. If you are a reader, please mercilessly mock bad articles and write numerous scornful letters to the editor until those editors pull out their beat-sticks and get slap-happy. We should not tolerate this kind of crap from people who are paid to get it right.

State the sample size and standard deviation?

I don't know how to make meaningful statistics with fewer data points.

MAD is more accurate than Standard Deviation to me, if you ask me.

> it is time to retire it from common use and replace it with the more effective one of mean deviation

> Standard deviation, STD, should be left to mathematicians, physicists and mathematical statisticians deriving limit theorems

>There is no scientific reason to use it in statistical investigations in the age of the computer, as it does more harm than good

He is saying it's not useful for real world things and people are just confused. What you wrote is reasonable view. What Taleb writes isn't.

There are those that dislike his ideas because it is threatening to their existing assumptions about probability and statistics. He argues that experts and majority of people do not account for the unpredictable but significant impact a single event can have which often shatters the commonly held belief. For example, swans were white until the discovery of black swans in Oceania, too big to fail multi-national corporations going bankrupt like Lehman's brothers and etc.

He's not anti-academic, but he is against teachings in the common academia that is based on naive assumptions that is specifically tailored to serve those that thrives most off the limited quantitative measures, such as market callers, hedge funds selling complicated quantitative algorithm trades, academics seeking fame and fortune by writing the most logical and quantitative paper without questioning any of the tools they are using, it is this hypocrisy and laziness that is apparent and those that try to deny to the point of making ad hominem remarks against a man, who simply observes these things and decides to write it in an entertaining manner (otherwise nobody would give a shit because the topic would be dry without lay man's linguo).

Keep an open mind, a lot of what he says I do find interesting ideas and it has influenced my thinking process quite a bit, however it's no way in anyway, grounds for cracking jokes or ridicule, in fact when I read some of the comments here, it's a bit shameful. We should be embracing new ideas in order to explore them, regardless of who the explosive nature of the claim, because the black swan event is very real and is not captured or understood completely by our current set of statistical tools and methodology based on questionable assumptions about how the real world operates. For example, 1/2500 chance is not what we really think it means in the real world because black swan events are more common than we think, a percentage probability do not fully reflect it's frequency and the magnitude of it's event.

Note the fall of crime rates in the United States following a decision to legalize abortion, economists and experts would come on television and bring up all sorts of random theories and ideas but little did they realize it was a chain effect from a court ruling passed decades ago until two economists came out with a paper that was ridiculed because it suggested that 'killing babies from poor neighbourhoods = lower crime rate' where most poor neighbourhoods is occupied by African Americans. Because such idea was earthshakingly controversial and still denied even to this day. Because Galileo claimed the earth was round instead of flat, he was executed. This is simply the nature of our world, almost all part of life, there exists a hierarchy that people simply do not ask questions either due to blind trust or the fear of reprisal.