
Post by krusader74 on Sept 9, 2015 7:15:27 GMT 6
Prob & Stat 101Questions about probability theory appear from time to time on these boards, especially in regard to dice rolls. I wanted to create a "Prob & Stat 101" thread where people could  Pose new probability and statistics questions
 Index past threads on prob & stat topics
 Discover free resources related to prob & stat such as dice rollers, textbooks, lectures, and software
 Share insights and interesting facts they've discovered



Post by krusader74 on Sept 9, 2015 7:16:30 GMT 6
An index of prob & stat related discussions5e: 2abilities: 1approximation: 15backgammon: 5binomial distribution: 11, 16calculators: 13chain rule: 7chainmail: 3chi square test: 5, 6complement rule: 7convolutions: 10cycle notation: 1d10: 6d20: 2, 6, 12d6: 9, 10, 11dF: 8dice: 1, 2, 6, 8, 9, 10, 11, 12, 13, 14, 15dice rollers: 14enumeration: 10fudge dice: 8gems: 4generating functions: 10knights and magick: 10knucklebones: 14loaded dice: 5, 6maximum: 2, 9, 12median: 12melee: 7, 17minimum: 2, 9, 12multiplication rule: 7order statistics: 2, 9, 12permutations: 1polynomials: 10product rule: 7rerolls: 1saving throws: 15simulation: 10, 17software: 3, 4, 5, 13, 16, 17statistical hypothesis test: 5, 6subtraction rule: 7tali: 14ushaped probability distributions: 1z10: 10zerobias notation: 10 1: 3d6, reroll 1'sKeywords: dice, rerolls, ushaped probability distributions, permutations, cycle notation, abilities 2: Advantatge/Disadvantage mechanicDescription: Calculate probabilities and statistics for 5E's advantage (and disadvantage) mechanic, i.e., max (and min) of two d20s. Keywords: dice, order statistics, d20, minimum, maximum, 5e 3: Alternative Post Melee MoraleDescription: Compute Chainmail post melee morale. Keywords: software, chainmail 4: Analysis of OD&D treasure typesDescription: Find the probabilities and statistics of 3LBB treasure types. Keywords: software, gems 5: Backgammon/Chess/CribbageDescription: Using the Chi Square test to check if dice are fair or loaded. Keywords: loaded dice, chi square test, statistical hypothesis test, software, backgammon 6: Dice question. Percentile?Description: Using two d10 as one d20. A post also mentions the use of the Chi Square test to check if dice are fair or loaded. Keywords: dice, d10, d20, loaded dice, chi square test, statistical hypothesis test 7: From ManToMan Melee tbl. to Weapon Factors vs. unarmoredDescription: Equiprobability of weapons to kill someone without armor in Chainmail. Keywords: complement rule, subtraction rule, chain rule, multiplication rule, product rule, melee 8: Fudge diceDescription: Fudge dice look like sixsided dice, but they are labelled 1, 0, and 1 twice. The notation is dF for rolling one fudge die, 4dF for rolling four fudge dice and summing. A roll of 4dF produces numbers in the range 4 to 4. What is their distribution? Keywords: dice, dF, fudge dice 9: Fuzzy on the statistics...Description: Probabilities and statistics for min {d6, d6} and max {d6, d6}. A very detailed discussion of order statistics towards the end of the thread. Keywords: dice, order statistics, d6, minimum, maximum 10: The math behind Knights and MagickDescription: Probabilities for z10 + d6. Keywords: dice, zerobias notation, convolutions, generating functions, polynomials, enumeration, simulation, d6, z10, knights and magick 11: Need Help With ProbabilitiesI'm math challenged!Description: [T]he probability for rolling a certain face value on a d6 a certain number of times or greater. Keywords: dice, binomial distribution, d6 12: Need some mathematical helpDescription: Roll three d20. Find the distribution of the maximum, median, and minimum. Keywords: dice, order statistics, d20, maximum, median, minimum 13: Number Appearing?Description: 30300 is what distribution? How to calculate? What's the mean and standard deviation? Keywords: dice, software, calculators 14: Palamedes, knucklebones and virtual dice rollersDescription: Introduces several topics  Palamedes, the mythical inventor of numbers, money, dice and gambling.
 Knucklebones, the mythical first dice game.
 Tali, dice made from animal bones with nonequiprobable outcomes.
 Palamedesan online dice roller, probability calculator and Bayesian belief net, authored by yours truly.
Keywords: dice, knucklebones, tali, dice rollers 15: Saving Throw DiceDescription: Convert saving throws from a d20 roll to a 3d6 roll or a d% roll. Keywords: dice, approximation, saving throws 16: Solo playDescription: Use the probabilities calculated in A Veteran's Odds + the binomial distribution to compute the outcome of a fight during solo play. Keywords: software, binomial distribution 17: A Veteran's OddsDescription: Level X fighter vs monster Y. What are the chances (mean and std. dev.) of a win, lose or draw? Keywords: simulation, software, melee



Post by krusader74 on Sept 9, 2015 7:20:11 GMT 6
Free prob & stat resourcesTextbooks & papersA Collection of Dice Problems with solutions and useful appendices (a work continually in progress) version September 3, 2015 by Matthew M. Conroy. 83 pages. PDF. Introduction to Probability (Version 2, 2008) by Charles M. Grinstead and J. Laurie Snell. 520 pages. PDF. Basic Probability Theory (1970) by Robert B. Ash. 350 pages. Tragically, Professor Ash was struck by a car and died this April. He put many fine math books online including this one on probability theory available as individual chapters or as one big PDF (78MB). Probability TheoryThe Logic of Science (1996) by Edwin Thompson Jaynes. As HTML or the first 3 chapters as a 95page PDF. Introduction to Probability and Statistics Using R (2010) by G. Jay Kerns. 412 pages. PDF. SoftwareWolframAlpha is a free online computational knowledge engine (or answer engine) with freeform natural language input. See the Statistics & Data Analysis examples. R is a freely available language and environment for statistical computing and graphics. Read the tutorial on probability distributions. GNU PSPP is a program for statistical analysis of sampled data. It is a free opensource replacement for the proprietary program SPSS. Maxima is a free crossplatform computer algebra system with support for probability distributions. See the online documentation on supported distributions. SciPy is a Pythonbased ecosystem of opensource software for mathematics, science, and engineering. The scipy.stats module provides excellent support for prob & stat computations. The Julia programming language provides an extensive mathematical function library, including support for probability distributions. LecturesStatistics 110: Probability by Joe Blitzstein at Harvard. 34 videos. ~50 minutes each. The best class on probability theory I've watched so far! Probability and Statistics: an introduction by Norman J. Wildberger at UNSW. 8 videos. ~50 minutes each. The shortest class on probability theory I've enjoyed so far. Math 131A: Introduction to Probability and Statistics by Michael Cranston at UC Irvine. The next OCW class I'm going to try to watch. Dice rollersAnydiceDicelabTroll dice roller and probability calculatorA miscellany of simulators, random generators and calculatorsChainmail Post Melee Morale  A JavaScript fiddle Graphical OD&D combat simulator in NetLogoLots of random generation...  A discussion thread on these boards. OD&D combat simulator in PerlPalamedes  JavaScript dice roller, probability calculator, and Bayesian belief net. Alpha quality. A list of probabilistic programming languages  A nontechnical introduction to probabilistic programming. Figaro, a free PPL that runs on the JVM, and a 91page PDF tutorial. Procedural Dungeon Generation AlgorithmViennese mazes  A Viennese maze is essentially a labyrinth in which passageways open and close in a periodic fashion. The linked page introduces the idea and provides a Python program for generating them.



Post by krusader74 on Sept 9, 2015 7:26:07 GMT 6
Review of standard probability theoryHere's a quick review of standard probability theory. Definitions The sample space Ω is the set of all outcomes ω
 A σalgebra ℱ ⊆ 2^{Ω}, called "events", satisfies:
 Ω ∈ ℱ
 ℱ is closed under complements: if A ∈ ℱ, then (Ω \ A) ∈ ℱ
 ℱ is closed under countable unions: if A_{i} ∈ ℱ for i=1,2,..., then (∪_{i} A_{i}) ∈ ℱ
 A probability measure P : ℱ → ℝ is a function that maps events to real numbers
 A probability space is the triple (Ω, ℱ, P)
 The probability that events A or B occur is the probability of the union of A and B: P(A ∪ B)
 The probability that events A and B both occur is the probability of the intersection of A and B: P(A ∩ B)
 Two events are mutually exclusive (or disjoint) if they cannot occur at the same time: P(A ∩ B) = 0
 The probability that event A occurs, given that event B has occurred, is called a conditional probability: P(AB) = P(A ∩ B) / P(B)
 The complement of an event is the event not occurring: P(A') = P(Ω \ A), where the backslash \ means set difference and the apostrophe ' means complement
 If the occurrence of event A does not change the probability of event B, then events A and B are independent and P(A ∩ B) = P(A) · P(B). Otherwise, the events are dependent.
Example: When you roll a d6, the sample space is Ω = {1,2,3,4,5,6}. If we define ℱ = 2 ^{Ω}, then there are 2 ^{6} = 64 events, because each of the six outcomes is either in or out (two possibilities) of a given event. Examples of events include:  No roll E_{none} = ∅
 A roll exactly equal to one E_{one} = {1}
 A roll of 5 or more E_{5+} = {5,6}
 An even roll E_{even} = {2,4,6}
 Any roll E_{any} = Ω
The probability measure is P(E) = E/6, where E is the number of outcomes in the event. The probabilities of the events listed above are  P(E_{none}) = 0
 P(E_{one}) = 1/6
 P(E_{5+}) = 1/3
 P(E_{even}) = 1/2
 P(E_{any}) = 1
AxiomsA probability measure P maps events to real numbers and satisfies the following three axioms:  Nonnegativity: ∀ E ∈ ℱ P(E) ≥ 0.
 Unitarity: P(Ω) = 1.
 Countable additivity (or σadditivity): P(∪^{∞}_{i=1} E_{i}) = Σ^{∞}_{i=1} P(E_{i}) if the E_{i} are mutually exclusive, i.e., E_{i} ∩ E_{j} = ∅ if i ≠ j.
Sometimes we're only interested in finitely additive probability spaces, in which case we replace the last axiom with 3'. Finite additivity: P(E _{1} + E _{2}) = P(E _{1}) + P(E _{2}) if E _{1} and E _{2} are mutually exclusive, i.e., E _{1} ∩ E _{2} = ∅. RulesThe following rules encapsulate the most important theorems deduced from the definitions and axioms:



Post by krusader74 on Sept 9, 2015 7:36:54 GMT 6
Exotic probabilitiesTinkering with the axiomsWhen we tweak the axioms of Euclidean geometry, we get nonEuclidean geometries. Euclid's fifth (or parallel) postulate says: If instead we assume there are no such parallel lines, then we get spherical geometry. And if instead we assume there are infinitely many parallel lines, then we get hyperbolic geometry. So what happens when we tweak the axioms of standard probability theory? We get what's called Exotic probability. The intent of this post isn't to present new research, but only to review the literature on exotic probability starting in 1930. NonunitarityLet's first relax the axiom that probabilities sum to one. We'll look at a couple of reallife situations. The first example is adapted from lecture #2 in Donald Saari's 16lecture course "Math 176: Math of Finance" given at UC Irvine. The second example is from lecture #3. (You can watch the whole course online here. The overall purpose of Saari's course is to develop the BlackScholes equation and its solution, stepbystep, starting from first principles. It's a great online class if you're interested in math and finance and you want to see where this difficult PDE came from, what it means and how to work out its solution.) Hedging exampleThe famous gladiator Aemilius Lepidus ("A" for short) is fighting in the gladiatora munera this weekend at the Forum Romanum. The Gothic mercenary Hrœdberð ("Bob" for short) and the Egyptian grain trader sšn ("Sue" for short) have vastly different opinions as to who will win the game, and they are each willing to wager on the outcome. Bob likes A to win the game. He is offering 5:3 odds. This means he will pay the gambler 5 gp for a 3 gp wager (and return the bettor his or her wager) in case A loses and nothing in case A wins. Sue thinks A will lose and offers 3:2 odds. You have 100 gp to bet with Bob and Sue. Problem How can you divide the 100 gp on wagers with Bob and Sue to ensure you always make money?
 What amount do you wager with Bob (the rest being bet with Sue) to maximize your winnings?
 What is this maximum amount that you win?
SolutionBob thinks A will win. Let x be the amount you bet with Bob that A loses. Then (100  x) is the amount that you bet with Sue that A wins. There are two cases to consider: Case 1: If A wins, you lose x with Bob, but you win 3/2 (100  x) from Sue. In this case, the profit curve is x + 3/2 (100  x) Case 2: If A loses, you win 5/3 x from Bob, but you lose (100  x) with Sue. In this case, the profit curve is 5/3 x  (100  x) Part 1. Find the feasible region by setting each profit curve greater than zero. On WolframAlpha, you can do this by typing the command solve x + 3/2 (100  x) > 0 && 5/3 x  (100  x) > 0 for x Click the Approximate form button to see the result as a decimal instead of a fraction: Result: 37.5 < x < 60 Any amount wagered in this feasible region ensures you make a profit! Let WolframAlpha compute the feasible region now by clicking this link: Part 2. Find the point in the range that maximizes your profit, by setting both profit curves equal solve x + 3/2 (100  x) = 5/3 x  (100  x) for x The result is x = 1500/31. Press the Approximate form button once, then the More digits button twice. You get: x ≈ 48.3870967741935. Let WolframAlpha compute the feasible region by clicking this link. Part 3. Find the maximum profit by substituting the value of x back into either one of the equations. For example: solve p = 5/3 x  (100  x) for p where x = 1500/31 Result: p = 900/31 ≈ 29.0323. Let WolframAlpha compute the feasible region by clicking this link. Horse racing exampleYou manage a race track. The three horses Alduin, Bowrider and Curse ("A", "B" and "C" for short) are about to race. The total amount wagered on horse A is 600 gp, on B is 300 gp and on C is 100 gp. How do you fix the odds on A, B and C to ensure you make 30 gp regardless of the outcome? Horse  Wagers  Odds  Profit curve (assuming given horse wins)  A  600 gp  X:1  600 X + 300 + 100 = 30  B  300 gp  Y:1  600  300 Y + 100 = 30  C  100 gp  Z:1  600 + 300  100 Z = 30 
Solve for X, Y, and Z. You can do this in WolframAlpha with the command Solve[{600 X + 300 + 100 == 30, 600  300 Y + 100 == 30, 600 + 300  100 Z == 30}, {X, Y, Z}] Let WolframAlpha compute the feasible region by copying and pasting the command. The result is X = 37/60 and Y = 67/30 and Z = 87/10. In decimals: X ≈ 0.61667 and Y ≈ 2.2333 and Z ≈ 8.7000 DiscussionIn the first example, your ability to profit no matter what happens springs from the fact that Bob and Sue's probabilities for the two different outcomes are incoherent: they sum to less than unity. Bob thinks there are 5 ways A can win and 3 ways A can lose. Therefore, he thinks the probability A will lose is 3/(5+3) = 3/8. Sue thinks there are 3 ways for A to lose and 2 ways for A to win. Therefore, she thinks the probability A will win is 2/(3+2) = 2/5. So the probability that A loses plus the probability that A wins is P(A') + P(A) = 3/8 + 2/5 = 31/40 ≈ 0.775 < 1. In the second case, where the bookmaker always wins, the sum of the probabilities P(A) + P(B) + P(C) = 37/(37+60) + 67/(67+30) + 87/(87+10) = 191/97 = 1.9690 > 1. Generalizing this, I claim that any time the probabilities sum to less than 1, an individual gambler can always profit. Conversely, if the sum of the probabilities > 1, then the system is rigged against gamblers, and the bookmaker always profits. NonnegativityNext, lets relax the axiom that probabilities can't be negative. We'll look at a halfcoin, described in the the paper "Half of a Coin: Negative Probabilities" by Gábor J. Székely: Székely developed the idea of a halfcoin using probability generating functions. Probability generating functionsA random variable X is just a function that maps an outcome to a real number, viz. X : Ω → ℝ. For example, the sample space for a coin toss is Ω = {heads, tails}. The random variable X that models a $1 payoff on a bet on heads is Given a random variable X, its probability generating function G(x) = p _{0} + p _{1} x + p _{2} x ^{2} + ... is a power series in the indeterminate x, whose coefficients p _{n} encode the probability mass function P(X=n). For example, let X be the random variable for a fair coin described above. Its pgf is 1/2 x ^{0} + 1/2 x ^{1} = (1 + x)/2 Key facts about pgfs:  If G is the pgf of a random variable X, then the sum of the probabilities is G(1).
 If G is the pgf of an rv X, then the expected (mean) value of X is E[X] = G'(1) where the prime symbol (apostrophe) denotes taking the first derivative.
 If G is the pgf of an rv X, then the variance of X is Var[X] = G''(1) + G'(1)  (G'(1))^{2}.
 If G is the pgf of an rv X and H is the pgf of an rv Y, then G · H is the pgf of X + Y.
HalfcoinsThis last key fact implies that the pgf of a halfcoin is the square root of the pgf of a fair coin, viz., √((1 + x)/2). We can use WolframAlpha to expand out this power series to get the coefficients: series (1/2 x^0 + 1/2 x^1)^0.5 You should get results that look like this: The referenced paper shows how to put the coefficients of this series into the following form: p _{n} = (1) ^{n1} √2 (C _{n1})/(4 ^{n}) for n=0,1,2,..., where the C _{n1} are the Catalan numbers, i.e., sequence A000108 in the OnLine Encyclopedia of Integer Sequences. Note that  there are an infinite number of coefficients. This means the halfcoin has an infinite number of sides.
 the probabilities p_{n} are negative for all positive even numbers n.
Halfcoins don't really exist. So what use are negative probabilities? There are important applications to physics and finance. PhysicsMark Burgin (2010)[4] pinpoints the genesis of negative probabilities in physics: Richard Feynman[5] offers the simplest explanation with his Roulette wheel example. Consider the following table of conditional probabilities: Result X  Condition A  Condition B  1  0.3  0.4  2  0.6  1.2  3  0.1  0.2 
Notice that the conditional probability P(X=1B) is negative. Feynman justifies this as follows: Now assume that P(A) = 0.7 and P(B) = 0.3. Then P(A) + P(B) = 1. We can calculate the probabilities of X as follows:  P(X=1) = P(X=1A) · P(A) + P(X=1B) · P(B) = 0.3 · 0.7  0.4 · 0.3 = 0.09
 P(X=2) = 0.78
 P(X=3) = 0.13
So even though the conditional probability P(X=1B) is negative, (∀ i) P(X=i) ≥ 0 and ∑ _{i=1}^{3} P(X=i) = 1, just as expected in standard probability theory. Feynman explains that if you calculate a negative probability, you may interpret it to mean:  the initial conditions can't be realized in the physical world, or
 the situation can't be observed directly
Heisenberg UncertaintyMore importantly for physics, if you calculate two correlated probabilities such that one is always positive when the other is negative, then it means you can't measure one situation at the same time as you measure the other. For example, Feynman gives us values of P(A) and P(B) such that  P(X=1) ≥ 0 whenever P(X=3) < 0 and
 P(X=1) < 0 whenever P(X=3) ≥ 0
This means you can't measure 1 at the same time you measure 3. Feynman comments: Nonunitarity revistitedFeynman sees a relationship between negative probabilities and nonunitarity: Extended ProbabilitiesMark Burgin[9] provides an axiomatization of negative probabilities. Without getting into details, the basic idea is to partition the sample space into positive Ω ^{+} and negative Ω ^{} parts such that the probability of an event occurring with its antievent is zero. In symbols: (∀ ω ∈ Ω ^{+}) (∃ ω ∈ Ω ^{}) P({ω, ω}) = 0 In other words, negative probabilities provide a convenient framework for letting us write down that two things can't happen together, like measuring a particle's position and momentum at the same time. FinanceIn finance, Macaulay duration is the weighted average maturity of cash flows. Since the cash flows may be positive or negative, we can reinterpret the formula for duration as the expected value of a probability distribution whose probabilities may be negative. Székely does this in his paper on the halfcoin.
[1]: Parallel postulate. In Wikipedia, The Free Encyclopedia. [2]: Dutch Book. In Wikipedia, The Free Encyclopedia. [3]: Gábor J. Székely, Half of a Coin: Negative Probabilities, Wilmott Magazine (July 2005), pp. 6668: [4]: Mark Burgin. Interpretations of Negative Probabilities (2010)[5]: Richard P. Feynman, Negative Probability (1987)[6]: Ibid., p. 4 [7]: Ibid., p. 6 [8]: Ibid., p. 3 [9]: Mark Burgin, Extended Probabilities: Mathematical Foundations (2009)



Post by The Perilous Dreamer on Sept 9, 2015 9:21:20 GMT 6
Now that is quite a resource! Have an Exalt!



Post by Finarvyn on Sept 9, 2015 10:12:24 GMT 6
The first couple of posts are awesome, but I'll confess that as I continued down the thread I started to get all glassyeyed. More probability than I have time to ponder, I suspect, but I love the idea of looking at probabilities and wish I had the time to study the topic further.
Part of my problem with probabilities is that I never really learned the theory to the level that I ought, but instead most of my probability calculations are done by a "brute force" method of creating tables and counting outcomes. Not so elegant, but works for a lot of what I need.



Post by krusader74 on Jul 31, 2017 14:10:06 GMT 6
Hothand fallacy diceWhat follows is a "show and tell" about some computer code I'm writing this week. I am rewriting and expanding Evan Verworn's Gambler's fallacy Dice, an ES6 (JavaScript) class for dice that follow the Gambler's fallacy. In addition to Gambler's fallacy dice in which people tend to predict the opposite outcome of the last event (negative recency), it also implements Hothand fallacy dice in which people tend to predict the same outcome of the last event (positive recency). (It implements a fair die too!) It adds convenience functions for making tables of probabilities, summarizing sample statistics, and performing Chisquared hypothesis testing. It uses ESDoc to document the API. I still have a liitle more work to do (see the Future research section near the end). When I'm done, I'll post the finished product on GitHub, which is having some problems with outages this week. I've attached the source code at the bottom, in case someone wants to try it now. (It works!) The following demonstration should help explain what this is all about... DemoIncluded in the source code is a script that demonstrates the main features. In the project's root directory, run the script from the command line by entering: $ node r markdowntable r chisquaredtest r ./src/Dice ./src/dicedemo.js or more simply as $ npm run demo The resulting MarkDownformatted output looks like this... hothanddice demoWe will be rolling three kinds of dice and testing them for fairness using Pearson's chisquared test: The null hypothesis H _{0} is that all faces are equally likely. The significance level α (alpha) is 0.001. We roll each die 10,000 times, compute the pvalue as p = 1  F(χ ^{2}_{5}), and reject H _{0} if p ≤ α. FairDieRoll 10 times and show the probability distribution for all 10 and the next... Roll  % of 1  % of 2  % of 3  % of 4  % of 5  % of 6  Actual Roll 

1  16.7  16.7  16.7  16.7  16.7  16.7  2  2  16.7  16.7  16.7  16.7  16.7  16.7  2  3  16.7  16.7  16.7  16.7  16.7  16.7  1  4  16.7  16.7  16.7  16.7  16.7  16.7  5  5  16.7  16.7  16.7  16.7  16.7  16.7  3  6  16.7  16.7  16.7  16.7  16.7  16.7  3  7  16.7  16.7  16.7  16.7  16.7  16.7  1  8  16.7  16.7  16.7  16.7  16.7  16.7  3  9  16.7  16.7  16.7  16.7  16.7  16.7  3  10  16.7  16.7  16.7  16.7  16.7  16.7  4  11  16.7  16.7  16.7  16.7  16.7  16.7  ? 
Roll once: 5 Generate sample of size 3: 5,5,5 Generate sample of size 9,986 (not shown) for a total of 10,000 rolls so far... Generate sample statistics for all 10,000 rolls... Statistic  Value 

sample size N  10000  sample mean m  3.49  sample standard deviation s  1.71  % of 1  16.83  % of 2  16.30  % of 3  17.18  % of 4  16.92  % of 5  16.07  % of 6  16.70 
pvalue: 0.406724960090882 (not significant: do not reject the null hypothesis that all faces are equally likely) GamblersDieRoll 10 times and show the probability distribution for all 10 and the next... Roll  % of 1  % of 2  % of 3  % of 4  % of 5  % of 6  Actual Roll 

1  16.7  16.7  16.7  16.7  16.7  16.7  4  2  18.2  18.2  18.2  9.1  18.2  18.2  4  3  18.8  18.8  18.8  6.3  18.8  18.8  1  4  5.3  21.1  21.1  10.5  21.1  21.1  2  5  9.5  4.8  23.8  14.3  23.8  23.8  4  6  12.5  8.3  25.0  4.2  25.0  25.0  6  7  16.7  12.5  29.2  8.3  29.2  4.2  5  8  21.7  17.4  34.8  13.0  4.3  8.7  1  9  4.2  20.8  37.5  16.7  8.3  12.5  3  10  9.5  28.6  4.8  23.8  14.3  19.0  1  11  4.0  28.0  8.0  24.0  16.0  20.0  ? 
Roll once: 2 Generate sample of size 3: 5,3,6 Generate sample of size 9,986 (not shown) for a total of 10,000 rolls so far... Generate sample statistics for all 10,000 rolls... Statistic  Value 

sample size N  10000  sample mean m  3.51  sample standard deviation s  1.71  % of 1  16.48  % of 2  16.82  % of 3  16.37  % of 4  17.08  % of 5  16.49  % of 6  16.76 
pvalue: 0.8290540016036037 (not significant: do not reject the null hypothesis that all faces are equally likely) HotHandDieRoll 10 times and show the probability distribution for all 10 and the next... Roll  % of 1  % of 2  % of 3  % of 4  % of 5  % of 6  Actual Roll 

1  16.7  16.7  16.7  16.7  16.7  16.7  1  2  28.6  14.3  14.3  14.3  14.3  14.3  6  3  25.0  12.5  12.5  12.5  12.5  25.0  3  4  22.2  11.1  22.2  11.1  11.1  22.2  3  5  20.0  10.0  30.0  10.0  10.0  20.0  1  6  27.3  9.1  27.3  9.1  9.1  18.2  1  7  33.3  8.3  25.0  8.3  8.3  16.7  1  8  38.5  7.7  23.1  7.7  7.7  15.4  6  9  35.7  7.1  21.4  7.1  7.1  21.4  3  10  33.3  6.7  26.7  6.7  6.7  20.0  1  11  37.5  6.3  25.0  6.3  6.3  18.8  ? 
Roll once: 3 Generate sample of size 3: 4,1,3 Generate sample of size 9,986 (not shown) for a total of 10,000 rolls so far... Generate sample statistics for all 10,000 rolls... Statistic  Value 

sample size N  10000  sample mean m  3.31  sample standard deviation s  1.80  % of 1  30.92  % of 2  0.17  % of 3  22.73  % of 4  9.79  % of 5  25.81  % of 6  10.58 
pvalue: 0 (significant: reject the null hypothesis that all faces are equally likely) Things to notice about the demo
 The gambler's die has the same statistical properties as the fair die: same mean, standard deviation, and (in the long run) the same probability distribution.
 In the statistical tests, the gambler's die tends to produce higher pvalues than the fair die (which equates with lower χ^{2} test statistics).
 The hothand fallacy is related to the Matthew Effect: "The rich get richer, and the poor get poorer (Mt. 25:29)" and the Simon Model. Success tends to flow to those who experience a little early success.
 The limit of the pmfs of the hothand die almost surely converge to a discrete powerlaw distribution, i.e., f(x)=a x^{k} for some permutation x of the faces. Note that a powerlaw distribution only has a welldefined mean if k>2, and it only has a welldefined variance if k>3.
Future researchFirst, it is most likely that the hothand die does not possess a welldefined mean or variance! What needs to be done is an estimation of the exponent of discrete power law data. Second, I want to implement a die that follows the Simon Model. Such a die generally works like a hothand die, except that on each roll there is a tiny probability that the die "grows" a new face! This kind of die also follows a discrete power law. Lastly, the number of rolls (10,000) in the demonstration was arbitrary. What really needs to be done:  Experimentation to find the effect size w = √(χ^{2}_{5}/n) for fair dice rolls. For a general χ^{2} distribution, the rule of thumb is 0.5 is a large effect size, 0.3 moderate, and 0.1 small. In the articles I reviewed (see the references below), it is generally assumed when we test dice we're looking for a large effect size (0.5). That assumption should be tested.
 A function N that maps significance (α), statistical power (1  β) and effect size (w) to sample size N = N(α, 1  β, w).
ReferencesHere are some references that go into more detail about testing dice for fairness as well as the relationship between sample size, effect size, statistical power and significance level for χ ^{2} distributions: AppendicesDependenciesTo run the source code, you will need: Development dependenciesTo continue development of this code, you should also have:
 ESLint for ES6 linting, run with
npm run lint
 ESDoc for documentation, run with
npm run doc
 Tape for testing, run with
npm run test
Attachmentshothanddice.zip (476.88 KB)  ZIP archive with all source code, documentation and tests



Post by krusader74 on Sept 12, 2017 17:58:03 GMT 6
How many times must you roll a die to test for fairness?I see this question raised a lot. But so far, I haven't seen a thoroughly satisfying answer. So I decided to try and write one... Effect sizeProblem: Too many statistical studies fetishize an arbitrary 0.05 significance level (alpha) such that a pvalue of 0.04999 is a publishable finding but p=0.05001 is not. The big problem with this is that even the most trivial effect will eventually become statistically significant if you test enough (because: the law of large numbers). Solution: The solution to this problem is a system for deciding precisely how large the effects in our data really are. In statistics, the effect size is used to describe a difference between two groups. Calculating effect sizeEach type of statistical test has its own way of calculating effect size. When we test dice for fairness, we typically use Pearson's chisquared (χ²) test, a type of Goodness of Fit test (of which there are many). The measure of effect size used for chi square tests is Cohen's w, defined as: Here the p _{0i} refer to the probabilities under the Null Hypothesis H _{0}, and the p _{1i} refer to the probabilities under the Alternative Hypothesis H _{1}. Example: You throw a sixsided die a bunch of times to test for unbiasedness. The Null Hypothesis H _{0} says that p _{0i}=1/6 for i=1 to 6. The Alternative Hypothesis H _{1} says that the p _{1i} ≠ p _{0i} for some of the i's. Remark 1: to actually compute w, we need concrete values for the p _{1i}, e.g., the die is loaded and the probability of throwing a six is 1/4 and the other sides are equiprobable. Remark 2: The null hypothesis states what's commonly assumed to be true. When we test a die for fairness, we generally assume all sides are equally probable. So if you're testing a d20 for faireness, then H _{0}: p _{0,i}=1/20 for i=1 to 20. However, if you happen to manufacture loaded d20s such that the "20" appears 10% of the time and the "1" never appears, and you want to test your loaded dice for quality control, then your null hypothsis won't be equiprobability. Instead it will be H _{0}: p _{0,1} = 0, p _{0,i} = 1/20 for i in 2 to 19, and p _{0,20} = 1/10. What is meant by "small" and "large" effect sizes?In Cohen's terminology, a "small" effect size is one in which there is a real effect  something is really happening in the world  but which you can only see through careful study. A "large" effect size is an effect which is big enough (or consistent enough) that you may be able to see it "with the naked eye." The following table summarizes what is meant by "small" and "large" in terms of Cohen's w: Type of effect  Effect Size (w) 

trivial effect  < 0.10  small effect  ≈ 0.10  moderate effect  ≈ 0.30  large effect  > 0.50 
Example: You throw a sixsided die a bunch of times to test for unbiasedness. If the die is really unfair and the percentage of times each face appears is given by the following table, then it has the effect size shown in the last column: Type of effect  % of 1s  % of 2s  %of 3s  % of 4s  % of 5s  % of 6s  Effect Size (w) 

trivial effect  14.27  16.67  16.67  19.07  16.67  16.67  0.083138  small effect  18.60  14.40  16.80  18.00  14.20  18.00  0.105451  moderate effect  14.70  20.70  16.50  7.10  21.00  20.00  0.291452  large effect  0.00  23.60  23.60  11.80  21.00  20.00  0.506454  (very!) large effect  3.30  0.00  93.30  0.00  0.00  3.40  2.058253 
PowerPower and sample size estimations are used to determine how many observations are needed to test the null hypothesis against an alternative. There are two types of errors we can make in a statistical study:
 A type I error is said to have occurred when we reject the null hypothesis incorrectly. The significance level (or "alpha") is the probability of making a type I error, i.e., α = P(reject H_{0}  H_{0} is true). Note: alpha is a conditional probability, and there are many common fallacies concerning them.
 A type II error is said to occur when we fail to reject the null hypothesis incorrectly. The chance ("beta") of making a type II error is β = P(fail to reject H_{0}  H_{1} is true).
The power of a test is the probability that the test correctly rejects the null hypothesis when the alternative hypothesis is true. Mathematically, power is defined as 1  β = P(reject H _{0}  H _{1} is true). It is the complement of beta. Note: Each type of statisical test has its own method for computing power (and/or beta). In Pearson's chisquared (χ²) test, the formula for power (and/or beta) is (**)where
 F is the is the cumulative distribution function (cdf) for the noncentral chisquare distribution
 nc is the noncentrality parameter of the noncentral chi distribution. It is defined as nc = n * w^{2}, where n is the sample size and w is the effect size.
 df are the degrees of freedom for the test. E.g., when you throw a sixsided die under a given hypothesis which defines the probability distribution, then df=5 because knowing 5 of these probabilities determines the sixth.
 x_{crit} is the critical value for the given value of alpha, i.e.,
Sample SizeTo determine the sample size required to test for an unbiased die, follow these steps:  Decide the significance level α. By convention, this is usually chosen to be 0.05, 0.01 or 0.001.
 Decide the power 1  β. By convention, this is usually chosen to be 0.80 or 0.90.
 Decide the effect size w you want your test to detect.
 Solve the above equation (**) for n.
The last step would be extremely difficult to compute by hand, given the complexity of the noncentral chi distribution. There are many solvers freely available online. I've written my own free, public domain Python module (chisq.py) to do the math. It computes:
 Effect size w, given Alternative Hypothesis (and optional Null Hypothesis, assumed to be equiprobable outcomes by default)
 Power 1  β, given w, n, df (and optional alpha, assumed to be 0.05 by default)
 Sample size n, given w, df (and optional alpha (0.05 by default) and power (0.80 by default))
The script requires the scipy.stats and scipy.optimize libraries. There's actually very little code in the script, since scipy does all the heavy lifting. If I find a decent JavaScript library that computes the noncentral chi distribution (or write my own someday...), then I will write a JavaScript version of these functions and put them online as a jsfiddle.



Post by foxroe on Sept 12, 2017 19:34:38 GMT 6
A fascinating thread as usual krusader74 , even if a bit over my head at times (and I'm an engineer!). A query, if you will (and this may already be answered upthread)... I'm thinking about a 3d6 sum rollunder mechanic where bonuses and penalties are applied with additional dice (usually one die, but sometimes two, and rarely three). In a bonus situation, (3+x)d6 are rolled, and the lowest three die are totaled. In a penalty situation, (3+x)d6 are rolled, and the highest three die are totaled. Hopefully that's clear. Now my question is, what would be the probability distribution of the 318 results in each case? I'm trying to determine if the "fiddliness" of the mechanic is offset by significant changes in the bell curve, or if I would be better off sticking with a simpler 3d6+x method. I tried looking into your Palamedes program, but I couldn't figure out what code to input to simulate the conditions.


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Post by sixdemonbag on Sept 12, 2017 23:53:12 GMT 6
Now my question is, what would be the probability distribution of the 318 results in each case? anydice.com/program/cea2Click "At Most" to get chances of success or "At Least" for chances of failure in your roll under system (it would be reversed in a roll over system). EDIT: Try the link again. I made some goofs.


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Post by sixdemonbag on Sept 13, 2017 0:19:55 GMT 6
Updated link in previous post to reflect changes.



Post by foxroe on Sept 13, 2017 0:35:33 GMT 6
Now my question is, what would be the probability distribution of the 318 results in each case? anydice.com/program/cea2Click "At Most" to get chances of success or "At Least" for chances of failure in your roll under system (it would be reversed in a roll over system). EDIT: Try the link again. I made some goofs. Sweet, exactly what I wanted! Thanks! Hmmm. It's not as elegant as I expected. The jump from 3d6 to 3d6+1d6 is significant, 3d6+2d6 less so, and 3d6+3d6 is negligibly different from 3d6+2d6. It makes a bigger difference at the extremes of the scale. I'll have to look into this some more and see how it compares to a straight 3d6+x mechanic.



Post by foxroe on Sept 13, 2017 1:12:23 GMT 6
OK, so... Using the "anydice" program to compare the two mechanics (a rollunder 3d6+xd6 pool mechanic versus a straight 3d6+x mechanic), it seems that in the dice pool mechanic, extra dice provide slightly more "bonus" for the first two dice, with successive die additions providing diminishing returns. However, I foresee issues with using it in actual play:  Negative bonuses and positive penalties may be nonintuitive for some players.  Players will likely find it easier to roll 3d6 and add or subtract a few points, than to roll a handful of dice and hunt for the three lowest/highest die.  Adding/subtracting points encourages more "spread", while adding more dice causes headaches (a subjective opinion, of course). And now back to your regularly scheduled thread.


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Post by sixdemonbag on Sept 13, 2017 1:52:55 GMT 6
People have trying to make dice pools happen for decades. It's always the sexy option. But, in my limited experience, linear probabilities just flow better at the table and from a design perspective.
Now, d100 is overkill to me (the very best humans can't discern much less than 1.5 normal cumulative variation or about 6.6% (VERY close to the d20 5% granularity). This is why the d20 is perfect. If you rolled a d20 30 times and announced "success" every time a 15 or higher was rolled, then did the same thing with a 14 or highter, would you be able to to pick out the subset of rolls that succeeded more often? Well in this case some people might be able to but not most. This puts the d20 right on the edge of human awareness which is perfect. Almost nobody could tell the difference between event A occurring 66% of the time and event B occurring 67% of the time. 1% is just too granular for humans.
Single d6 rolls match up with 0.5 normal distribution intervals almost perfectly which is an amazing coincidence. This is why the d6 gets so much love. It's the best die for everyday modelling and observations. The five degrees of nonzero granularity that a d6 provides fits the way nature works the best. I believe that we humans can sense that somehow with the d20 approaching our limits of casual observation.
Thus, straight d20 and d6 rolls hit the sweet spot for me. If your design is made so that the pool size never changes (monopoly, yahtzee, reaction checks, etc.) and the extremes aren't much less 6.6% then they can work out great. So maybe don't give up quite yet.


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Post by sixdemonbag on Sept 13, 2017 3:10:33 GMT 6
For reference:
Cumulative normal distributions in 0.5 intervals: 16% 31% 50% 69% 84%
Compared to d6: 17% 33% 50% 67% 83%
That's a d**n near perfect match!!!



Post by krusader74 on Sept 13, 2017 8:15:47 GMT 6
I'm thinking about a 3d6 sum rollunder mechanic where bonuses and penalties are applied with additional dice (usually one die, but sometimes two, and rarely three). In a bonus situation, (3+x)d6 are rolled, and the lowest three die are totaled. In a penalty situation, (3+x)d6 are rolled, and the highest three die are totaled. Hopefully that's clear. Now my question is, what would be the probability distribution of the 318 results in each case? Really great question, foxroe !! sixdemonbag already posted a very practical answer, using anydice.com. So let me try to provide additional theory and some computational alternatives, since I'm sure others will ask questions about this same distribution in the future. The probability mass function f(x) of the sum of the khighest of n ssided dice is given by an extremely unwieldy combinatoric formula: where and The formula was derived by a user called "techmologist" on Physics Forums. To see the stepbystep derivation of this formula along with further explanation and analysis, please read the linked 2page article: But this only answers part of your question, because
 you also want the distribution for rolling n ssided dice and keeping the lowest k
 you really want the cdf, not the pmf, since you're thinking about a "rollunder mechanic"
To answer these two concerns succinctly: First: The pmf for rolling n ssided dice and keeping the lowest k looks like the mirror image of the pmf for rolling n ssided dice and keeping the highest k. For example, the pmf g for rolling 4 6sided dice and keeping the lowest 3 looks like the pmf f for rolling 4 6sided dice and keeping the highest 3 after you swap the 18 with the 3, swap the 17 with the 4, and so on. In other words if π is the permutation: then g = f ∘ π Note that π is an involution, i.e., π∘π= 1 or equivalently π=π ^{1}, so that f=g∘π too. Second: If f(x) is the pmf of the discrete random variable X, then its cdf is F(x) = P(X ≤ x) = ∑ _{t≤x} f(t) If you were a masochist, you could write out the cdf F(x) for 4d6k3 in full to see what it looks like. It looks ugly. Very, very ugly. You'd see (among other things), five nested summation symbols. You really want a computer to manipulate these functions for you... I tried looking into your Palamedes program, but I couldn't figure out what code to input to simulate the conditions. Not your fault! Palamedes is highly idiosyncratic. For example, you can't use the letter "x" as a variable name, because it is the syntax for an exploding die. Furthermore, Palamedes isn't finished or fully debugged yet. Nevertheless, you can get the Prob & Stat data you need from it, because it implements techmologist's formula. Here are some input examples: 4d6k3 /* generate a random variate */ cdf(4d6k3) ∘ 18 /* evaluate cdf at 18, returns 1 */ p(4d6k3 ≤ 18) /* same as above, only more pleasing notation */ table(p(4d6k3)) /* table for the pmf */ table(cdf(4d6k3)) /* table for the cdf */ table(QQ(cdf(4d6k3))) /* table for the cdf as fractions, not decimals */ table(stats(p(4d6k3))) /* mean, variance, etc. */ barchart(p(4d6k3)) /* plot of the pmf */ barchart(cdf(4d6k3)) /* plot of the cdf */ perm < [3..18] > [18..3] /* the permutation shown above */ roll_4d6_keep_lowest_3 < p(4d6k3) ∘ perm /* explained above */ p(roll_4d6_keep_lowest_3 ≤ 18) /* cdf, returns 1 */ p(roll_4d6_keep_lowest_3 ≤ 3) p(roll_4d6_keep_lowest_3 < 0) /* returns 0 */ table(roll_4d6_keep_lowest_3) table(cdf(roll_4d6_keep_lowest_3)) table(stats(roll_4d6_keep_lowest_3)) barchart(roll_4d6_keep_lowest_3) barchart(cdf(roll_4d6_keep_lowest_3)) And here is the (really long...) output from running this script: You can PM me with followup questions. Anyway, I hope this helps!


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Post by sixdemonbag on Sept 13, 2017 19:06:34 GMT 6
You are making us all look bad krusader74 ! Lol, very nice walkthrough there. I don't understand it all but it's really cool of you laying things out like that. Above and beyond!



Post by foxroe on Sept 13, 2017 19:51:51 GMT 6



Post by krusader74 on Sept 27, 2017 18:18:49 GMT 6
Are dice throws predictable?Dice throws are deterministic mechanical systems. But determinism does not imply predictability: They may be chaotic, in which case, a small, immeasurable perturbation in the initial conditions would lead to drastically different, unpredictable outcomes. To answer this question, you first write out the equations of motion for dice being thrown and bouncing on a surface. I won't bore you with the equations... you can see them in the references at the end. These equations merely encapsulate the physical laws governing the system:  Conservation of mass
 Conservation of energy (potential + kinetic)
 Conservation of momentum (translational and angular)
Then you check for sensitive dependence on initial conditions: Do trajectories with different initial conditions separate exponentially? In the case of dice, it turns out the answer to this question is NO, as shown in the following two papers: 1. Dice Throw Dynamics Including Bouncing (XXIV Symposium Vibrations in Physical Systems, 2010) by Juliusz Grabski, et al. Quote: 2. Iteratedmap approach to die tossing ( Physical Review A, Volume 42, No 8, 1990) by R. Feldberg, et al. Quote:



Post by waysoftheearth on Mar 21, 2019 22:40:12 GMT 6
For reference: Cumulative normal distributions in 0.5 intervals: 16% 31% 50% 69% 84% Compared to d6: 17% 33% 50% 67% 83% That's a d**n near perfect match!!! Agree it's "near enough" to be interesting, but what do you suggest is its utility? Okay, so I kinda get that instead of throwing 600 weirdshaped dice I could boil them all down to 1d6 and then confidently declare: "A two! This implies our 600 throws would have yielded a result between 1 and 0.5 standard deviations below the mean." but... unless I've already got a credible sample of outcomes for the 600 dice thing I want to abstract (e.g., combat?) to a single d6, does this help much? Happy to be educated


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Post by sixdemonbag on Mar 30, 2019 1:47:23 GMT 6
Agree it's "near enough" to be interesting, but what do you suggest is its utility? Oh boy, you are asking the wrong person for any actual utility! Going back and rereading this thread, I think it was nothing more than idle curiosity. However, now that you have me thinking, you could build an entire game (let's say an RPG) around a single d6 throw and it would feel somehow "natural." So, in theory, you could boil down a lot the progression tables and such into a simple: Players succeed/hit/save on a 3 or higher and monsters succeed/hit/save on a 5 or higher. No other bonuses besides the purely situational would be strictly necessary for an entire campaign. An evil thought experiment: 1. Get an online game of D&D going where the ref makes all the rolls 2. For every PC action, hit, or save, just roll a d6 to determine success. Let's say 4 in 6, maybe adjusted by situation. 3. Don't tell them you are doing this, let them assume that you are using d20's and class tables. 4. For monsters, cut this in half and assume they succeed, hit, or save with 2 in 6 odds. 5. Do you think the players will ever notice? 6. Will they have fun? 7. Did this provide enough variety of outcome and yet still feel satisfying and not totally predictable at the same time? 8. Did this provide any actual surprises that enhanced the fun and excitement? My hypothesis is that this type of setup would actually be fun to play and provide enough randomness and a sense of player "competency" to be fun. Maybe the exact odds would need to be adjusted, but with a d6 modeling nature so closely, this would feel pretty natural no matter which direction you went in. You could do this with any die, of course, but with the d6, the math is already done for you, with just enough granularity to not be noticeable. Now, there's really no practical reason to do this, but some enterprising game designer could take these principles and make a simple and fun game out of it. You could use the d6 and take advantage of casino slot machine variable ratio schedules to find the maximum addictive odds of success. Using human psychology, for instance, most slot machines are programmed so that 925% of pulls are "winners." Less than this and players get frustrated at leave. More than this, and the payout has to be too small to attract further longterm play. For an RPG, it would be curious what the best VR (mean number of die throws per success) for addictive play turns out to be. I believe WOTC did some research via playtests and found it to be around 6570% (I can't seem to find a link anywhere but I swear I read somewhere that they did this research???), otherwise the players felt like they were "missing" too often. This happens to be very close to 4 in 6 as above and reversed as 2 in 6 for monsters. Something about being right around a std. dev. seems to just "feel right." This is long and rambling, so I apologize in advance for the wasted electrons...

