Roulette I've seen a new absurd claim that the gambler's fallacy isn't.....

You have not a clue what you're saying.
Bi-nomial means the distribution of outcomes of a yes-no question (success vs failure) only TWO possibilities win vs lose or an event I CHOOSE. No guesswork. It provides the probability of the MINIMUM number of wins that I can expect in N trials. It is NOT probabilities of data samples with "too many numbers", "some more likely" and "others not at all" handwaving nonsense.

Again the binomial distribution function tells everyone PRECISELY the worst case they can expect over N spins with a DEFINED probability.

I've given you the facts, I've requested you to confirm calculations and the best you can do is handwaving with "gazllions" and it's "still possible".

Clearly the math escapes you.

So again you clearly do not understand what you're saying. The math says whatever discrete event I have chosen, be it even chance, dozens, streets, pairs, singles, whatever, it will occur a MINIMUM number of times with a DEFINED probability over N spins, past, present or future. It is calculated from the constant probability of the ball landing in one of 37 pockets on a roulette wheel.

You got your schooling on probability there did you?
Maybe you can read what it says there:

Binomdist() function
This Excel function gives binomial distribution probabilities. The format is =BINOMDIST(x,y,p,0), where x is the number of successful trials, y is the number of total trials, and p is the probability of success of each trial).

For example, suppose you wish to know the probability of rolling exactly 25 sevens in 100 rolls of two dice. The answer is =binomdist(25,100,1/6,0) = 0.009825882. If you put in a 1 for the last term, instead of a 0, you will get the probability of 25 or fewer sevens.
​

Q: How many WoV clowns does it take do the grown ups math?

 

Meanwhile, to make myself clear, I invite to make sure & see if something's really there .. although a lot are .. not everyone. Its rare to find someone intelligent enough to talk to & you ain't one of them. When you grow a pair & you have something to say, say it straight in the face; for now you act like a dear in a bush looking & meeking out .. thinking you appear so smart. & lastly, mind your own business.

 

Haven't seen binomial distribution applied this way before, usually it's discussed as a way to invalidate short-term positive variance that "lucky" players have misconstrued as +EV.

Good stuff TwoUp, not the usual forum retreaded info.

 

& what do you base your conclusion on .. what possible insight into lucky's game do you have ?
Unless your intent it to turn away as many as you can with intent to conceal .. wouldn't be surprised.

 

None, in terms of binomial distribution, I have no expertise in that area. It's interesting for sure though, especially to a neophyte like myself.

The rest of the post I don't understand, not sure why you're upset but whatever the reason is, no hard feelings. If it's about fathead's coding post, I'm sure you can see our point of view in terms of people demanding things from those who provide those services for free as being out of order.

Again, as with my post about Dutch, no harm intended either way.

Cheers

 

Twoup,

Again, knowing what the worst case you can expect will in NO WAY whatsoever help you change the odds! I don't know why this is so hard for some people to comprehend.

The gambler's fallacy, also known as the Monte Carlo fallacy or the fallacy of the maturity of chances, is the incorrect belief that, if a particular event occurs more frequently than normal during the past, it is less likely to happen in the future (or vice versa), when it has otherwise been established that the probability of such events does not depend on what has happened in the past. Such events, having the quality of historical independence, are referred to as statistically independent. The fallacy is commonly associated with gambling, where it may be believed, for example, that the next dice roll is more than usually likely to be six because there have recently been fewer than the usual number of sixes. https://en.wikipedia.org/wiki/Gambler's_fallacy


Binomial distribution
From Wikipedia, the free encyclopedia



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"Binomial model" redirects here. For the binomial model in options pricing, see Binomial options pricing model.
See also: Negative binomial distribution
Binomial distribution
Probability mass function

Cumulative distribution function

Notation {\displaystyle B(n,p)}
Parameters {\displaystyle n\in \{0,1,2,\ldots \}} – number of trials
{\displaystyle p\in [0,1]} – success probability for each trial
{\displaystyle q=1-p}
Support {\displaystyle k\in \{0,1,\ldots ,n\}} – number of successes
PMF {\displaystyle {\binom {n}{k}}p^{k}q^{n-k}}
CDF {\displaystyle I_{q}(n-k,1+k)}
Mean {\displaystyle np}
Median {\displaystyle \lfloor np\rfloor } or {\displaystyle \lceil np\rceil }
Mode {\displaystyle \lfloor (n+1)p\rfloor } or {\displaystyle \lceil (n+1)p\rceil -1}
Variance {\displaystyle npq}
Skewness {\displaystyle {\frac {q-p}{\sqrt {npq}}}}
Ex. kurtosis {\displaystyle {\frac {1-6pq}{npq}}}
Entropy {\displaystyle {\frac {1}{2}}\log _{2}(2\pi enpq)+O\left({\frac {1}{n}}\right)}
in shannons. For nats, use the natural log in the log.
MGF {\displaystyle (q+pe^{t})^{n}}
CF {\displaystyle (q+pe^{it})^{n}}
PGF {\displaystyle G(z)=[q+pz]^{n}}
Fisher information {\displaystyle g_{n}(p)={\frac {n}{pq}}}
(for fixed {\displaystyle n})
Part of a series on statistics
Probability theory

• Probability
  • Axioms
• Determinism
  • System
• Indeterminism
• Randomness

• Probability space
• Sample space
• Event
  • Collectively exhaustive events
  • Elementary event
  • Mutual exclusivity
  • Outcome
  • Singleton
• Experiment
  • Bernoulli trial
• Probability distribution
  • Bernoulli distribution
  • Normal distribution
• Probability measure
• Random variable
  • Bernoulli process
  • Continuous or discrete
  • Expected value
  • Markov chain
  • Observed value
  • Random walk
  • Stochastic process

• Complementary event
• Joint probability
• Marginal probability
• Conditional probability

• Independence
• Conditional independence
• Law of total probability
• Law of large numbers
• Bayes' theorem
• Boole's inequality

• Venn diagram
• Tree diagram

• v
• t
• e

Binomial distribution for {\displaystyle p=0.5}
with n and k as in Pascal's triangle

The probability that a ball in a Galton box with 8 layers (n = 8) ends up in the central bin (k = 4) is {\displaystyle 70/256}.
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p). A single success/failure experiment is also called a Bernoulli trial or Bernoulli experiment, and a sequence of outcomes is called a Bernoulli process; for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance.[1]

The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, the draws are not independent and so the resulting distribution is a hypergeometric distribution, not a binomial one. However, for N much larger than n, the binomial distribution remains a good approximation, and is widely used.

 

The idiot juice is in high form. He really wants to draw in the maggots to do his dirty work.

 

The only math you know is cut and paste basic math.

Why don't you check out the first degree syllabus, then cut and paste?

 

At least this cut and paste math is factual unlike your own words ignorant first post. Which you use to fool members here.

 

Lucky, Twoups,

I can tell you guys are probably yet another group of system junkies that are still trying to find "just the right" negative progression to make your systems work. You two are wasting your time.



You can't use math to beat the random game of roulette.

 

You seem to be arguing against probability itself if you disagree what the binomial probability distribution calculation repesents and how it is formulated. It is based on the event probability remaining CONSTANT across independent events.

Not gamblers fallacy in the slightest as clearly you can't grasp the math.

You need to admit to yourself that you have cognitive dissonance as you believe yourself an elite where you enjoy telling people about WoV math-isms and gamblers fallacy troupes you picked up along the way, and now someone is beating you over the head with actual math.

You could have copied and pasted the binom.dist formula from the hallowed halls of WoV into excel to see the answer for yourself. All too confusing for you, so you just paste stuff from Wikipedia.

You appear to believe a coin flipped 200 times is just as likely to have a distribution close to 50/50 as it is 200/0, which is clearly wrong. There are many more combinations where heads and tails are roughly balanced and very few (1 exactly) where it's all heads. Even a child can grasp this.

 

Either you can't read or you have a habit of telling lies. Besides math ignorant where you google, copy and paste.

I wrote flatbet.

Your response,

 

I think we ALL understand what probability, binomial distribution etc is, so there is really no need to argue.
Let's look at a simple case so as not to cloud the issue. What's the probability of at least 2 reds in 20 spins? It's high! Does that help you to win? No.

 

So you accept that ANY set of spins, the probability distribution holds with a certainty defined by the binomial distribution function.

Any set of 20 independent spins will have a certain population of reds, any historical set of 20 spins will have a certain population of reds, even a set of results randomly plucked from historical results will have the same expected distribution within the given certainty. So will any set of future results.

Good so far?

The binomial distribution function says for an event with 18/37 probability (roulette even chance) that I can expect AT LEAST 3 or more reds within ANY 20 independent spins and those reds will exist somewhere in the set with an exact probability that I will be right 3,228 times for every time I am wrong (99.969% confidence).

Does it really take that much imagination to identify situations where density of reds must be higher and actually be right many more times right than wrong, and that you can even design a staking plan around it?

If we accept the binomial distribution function is correct we must therefore also accept the important critical assumptions:

1. constant probability, the probability cannot be changing, it must be fixed. No fallacies.
2. the events are completely independent.
3. ordering of events is irrelevant, past or future is irrelevant.

Remember that the distribution probability IS NOT the probability of past events or future events, it is the probability of number of occurances over a set of independent events as a whole.

So to close this off, no, this is not gamblers fallacy where probability magically changes. That claim is false based on the very definition of the binomial distribution calculation, as there are no features in the equation that manipulate the probability of each discrete event or make one event dependent on another.

It is this very function that describes how expectations must get narrower and converge on the inherent probability of each event as the number of trials increases. Over many thousands or millions of events the confidence goes up and rapidly approaches 100% (but never exactly) and it is why the casino expects over the long haul that reds and blacks will be closely balanced, but not exactly.

One must therefore either accept that probability is described mathematically, or you say "no, I don't like it, I've been told that the earth is flat", and we are back in the land of ignorance and mysticism.

What I have described may chaff with those "elites" who get pleasure calling fallacy everywhere. Sure most people who believe in fallacies are technically wrong and misguided and don't have the mathematical knowledge to support their wishful and dangerous progressions but neither do all the naysaying fools have the mathematical knowledge or creativity to exploit probabilistic events.

I have shown there actually is a mathematical basis for gamblers fallacies, however imperfectly or mathematically illiterate gamblers may describe them, their intuition is not exactly wrong. The expert says "it's a fair wheel" but then says "well it can be unfair alot of the time", those statements cannot both be true surely? Intuitively people say it has to balance out at some point if it's fair. However they just don't realise the events never get back in balance, the "error" just gets drowned out and becomes a decimal point over many many more trials than they could concieve. That right there is the binomial distribution function that tells how confident we can be in things being what we expect based on the inherent probability. The math shows its worse than most people can imagine, we can only expect 3 reds in 20 spins for a roughly 50/50 event with a moderate degree of confidence.

Yes it is impractical to exploit with a martingale but I'm not suggesting doing that. Be creative.

 

@ Gizmo,

HE is not the Lone Idiot in this topic .

 

Who you do or don't listen to / learn from is your own choice.
The only people who lose are the people who refuse to learn.
The whole nonsense thread can be summed up in one flat betting photo.


Choose what side you are on wisely, or else you're just wasting your own time.
If it's really a fallacy then this is impossible... yet it isn't. So make your own decision on who is right.

 

There's no button to like your post. That's a good explanation.

 

Have been saying if done correctly it wins flatbet. Btw TG taught me to investigate the math, finally got it right. Thanks.

 

No it just requires another mentally challenged fool that sucks at math to get sucked in like you have. Again, more gambler's fallacy. The same number of pockets remain on the wheel from one spin to the next, so why would the odds change?


"The gambler's fallacy, also known as the Monte Carlo fallacy or the fallacy of the maturity of chances, is the incorrect belief that, if a particular event occurs more frequently than normal during the ..."- Wikipedia

 

It's ok to be wrong. Just accept it and move on.