One last try

[mikechops]

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So I guess first step would be to prove that "luck" actually is normally distributed. How do you know it really doesn't follow a Cauchy distribution?

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Luck (at poker at least) DOES follow a normal distribution. See the central limit theorem.

http://en.wikipedia.org/wiki/Central_limit_theorem

[FeltBelt]

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Every process in life follows a distribution pattern known as a bell curve.

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Well, no it doesn't. In fact, math has many distributions it uses to describe processes found in life. The one you refer to as "bell curve" is really known as a normal or Gaussian distribution. Here is a listing of others that are used to describe many life processes from proton decay to the patterns of relected light, etc:

Benford • Bernoulli • binomial • Boltzmann • categorical • compound Poisson • degenerate • Gauss-Kuzmin • geometric • hypergeometric • logarithmic • negative binomial • parabolic fractal • Poisson • Rademacher • Skellam • uniform • Yule-Simon • zeta • Zipf • Zipf-Mandelbrot Ewens • multinomial • multivariate Polya
Beta • Beta prime • Cauchy • chi-square • Dirac delta function • Erlang • exponential • exponential power • F • fading • Fisher's z • Fisher-Tippett • Gamma • generalized extreme value • generalized hyperbolic • generalized inverse Gaussian • Half-Logistic • Hotelling's T-square • hyperbolic secant • hyper-exponential • hypoexponential • inverse chi-square • inverse Gaussian • inverse gamma • Kumaraswamy • Landau • Laplace • Lévy • Lévy skew alpha-stable • logistic • log-normal • Maxwell-Boltzmann • Maxwell speed • normal (Gaussian) • normal inverse Gaussian • Pareto • Pearson • polar • raised cosine • Rayleigh • relativistic Breit-Wigner • Rice • shifted Gompertz • Student's t • triangular • type-1 Gumbel • type-2 Gumbel • uniform • Variance-Gamma • Voigt • von Mises • Weibull • Wigner semicircle • Wilks' lambda Dirichlet • Kent • matrix normal • multivariate normal • multivariate Student • von Mises-Fisher • Wigner quasi • Wishart
• Cantor • conditional • exponential family • infinitely divisible • location-scale family • marginal • maximum entropy • phase-type • posterior • prior • quasi • sampling • singular

So I guess first step would be to prove that "luck" actually is normally distributed. How do you know it really doesn't follow a Cauchy distribution?

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OMG you are so S-M-R-T! Your Google skillz are leet!

D-bag.

[Dov]

At the risk of upsetting people for bumping this thread, I would like to add the following:

To those in this thread that responded or agree that luck doesn't exist because results that fall within an expected outcome set are accounted for by the probabilities, I would like to point out that this is exactly how luck is measured.

The less probable the event, the luckier the beneficiary of the unlikely outcome. The converse is also true. The less likely the event, the more unlucki the party that was somehow injured by the outcome.

To the OP:

I think that you may not realize just how difficult randomness can be for people to understand. Your attempt to quantify randomness in the way you did failed for a number of reasons, not the least of which is that it seems like you started with your conclusion and tried to find the facts to support it later.

If you really want to try to prove your theory, you should use a significant sample size of all relevant data, and then draw your conclusions from it, not the other way around.

People's brains handle patterns very well, but not randomness. I suggest that you stop trying to find patterns where none exist.

Good Luck

[holyfield5]

[ QUOTE ]
At the risk of upsetting people for bumping this thread, I would like to add the following:

To those in this thread that responded or agree that luck doesn't exist because results that fall within an expected outcome set are accounted for by the probabilities, I would like to point out that this is exactly how luck is measured.

The less probable the event, the luckier the beneficiary of the unlikely outcome. The converse is also true. The less likely the event, the more unlucki the party that was somehow injured by the outcome.

To the OP:

I think that you may not realize just how difficult randomness can be for people to understand. Your attempt to quantify randomness in the way you did failed for a number of reasons, not the least of which is that it seems like you started with your conclusion and tried to find the facts to support it later.

If you really want to try to prove your theory, you should use a significant sample size of all relevant data, and then draw your conclusions from it, not the other way around.

People's brains handle patterns very well, but not randomness. I suggest that you stop trying to find patterns where none exist.

Good Luck

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Luck is fallacy, just people taking probaility personally.

people see a 2 outer and they cry out omg you are the luckiest guy ever and i am unlucky no matter how many times their hand has held up in the situation.

[DcifrThs]

it has been a while since ive seen worse logic.

congrats.

Barron

[Dov]

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Luck is fallacy, just people taking probaility personally.

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I understand.

That's what I was getting at when I said that it's very hard for people to understand random events.

You are correct in that they take it personally and have selective memories.

IMO, It is still somewhat useful to represent random events on a scale of lucky vs unlucky for people who are still unable to grasp the entire concept, just as a means of conveying the concepts. In this case, I am just substituting the word lucky for probable, basically.

Since they understand lucky, but not probable, initially, I have found that they become more receptive to hearing you out, if you're trying to educate them or take you seriously if you're trying to confuse them. (like during a game)

Selective memory and small sample sizes will get you every time.

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people see a 2 outer and they cry out omg you are the luckiest guy ever and i am unlucky no matter how many times their hand has held up in the situation.

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I don't often find myself in this situation anymore, but when I do, I like to tell someone who gets upset that I caught a lucky card that I was 50/50 to make the hand.

Either I make the hand, or I don't.

This usually improves my image at the table quite well and often will further tilt the guy who suffered the beat.

In addition, I don't look like an a-hole doing it.

[sweetjazz]

Craig, did you know that the width of the bell curve is inversely proportional to the square root of the number of samples taken?

Basically, the width of the bell curve is measured by the quantity called standard deviation. If you graph the distribution of a player's observed winrate after N hands, the standard deviation will be equal to the standard deviation involved in playing 1 hand (which can be easily estimated, for example PokerTracker does this) divided by the square root of N. If you play enough hands (say hundreds of thousands), eventually the standard deviation becomes so small that the bell curve looks like a spike when you graph it on the same axis as you would graph the bell curve of, say, a 100 hand sample.

I've only skimmed your threads, but this seems to be what you're failing to grasp.