PrepShorts · Study sheet · Class 9 Mathematics · Chapter 7, The Mathematics of Maybe: Introduction to Probability
Chapter 7 · The Mathematics of Maybe: Introduction to Probability
Fair, unbiased, and memoryless: the gambler's fallacy
This video could not be loaded. Reload the page to try again.
Sign in with Google10 min.
Keep your place in this chapter — sign in, it’s free.Sign in
After six heads, tails is not due. The proportion settles by dilution, not by correction — a surplus is divided, never repaid.
The idea
The gambler's fallacy is not an arithmetic slip. It is a wrong theory about where long-run stability comes from. People correctly believe that the proportion of heads settles near a half, and then wrongly conclude that the coin must be repaying a debt to make it happen. It settles by dilution, not by correction: a run of six heads is never cancelled, it is simply divided by a growing number of tosses, so after a thousand more tosses the same surplus of three has become a rounding error. That is why a coin with no memory is not merely compatible with a stable long-run frequency — it is exactly what produces one. And "fair" and "unbiased" are the chapter's names for the physical symmetry that supplies the equal chances the whole argument starts from.
What you should be able to do
- State the gambler's fallacy and identify it in a described piece of reasoning
- Explain why the probability of the next toss is unchanged by any run of earlier results
- Explain how a long-run proportion can settle towards a half without any outcome being owed, using a numerical dilution argument
- State what would have to be true of a coin for the fallacy to be correct, and say what that would make the coin
- Define what makes a coin fair and unbiased, and say what a random toss adds to that
- Give a working definition of independent trials
- Apply the reasoning to a die in a board game, and to a coin experiment already recorded
- Explain what a long run of one result is evidence about, and what it is not evidence about
Words to know
| Term | Definition in one line | First introduced |
|---|---|---|
| Gambler's Fallacy | the belief that a result which has not come up for a while is more likely next | printed in bold, and as the heading of its own box, on p. 164 |
| fair | of a coin that is symmetrical, so no side is favoured | printed in quotation marks, with its definition, in the FAIR AND UNBIASED box (p. 164) |
| unbiased | the chapter's name for that property of the coin | printed in quotation marks, with its definition, in the FAIR AND UNBIASED box (p. 164) |
| random toss | a toss in which the coin falls freely, without interference | printed in quotation marks, with its definition, in the FAIR AND UNBIASED box (p. 164) |
| symmetrical | shaped so that neither side has any advantage | printed in the FAIR AND UNBIASED box (p. 164) |
| independent event | one whose probabilities are unaffected by what happened earlier | printed in Example 6 (p. 164); used again of trials in §7.4 (p. 168), and defined in neither place |
| no memory | the chapter's phrase for the coin not tracking its past | printed twice in the GAMBLER'S FALLACY box and again in Example 6's Key Idea (p. 164) |
| Law of Large Numbers | the fact that the observed proportion settles towards the theoretical value | printed in bold at the close of §7.2.3 (p. 163) |
| long run | the many-trials regime the law describes | printed in the Think and Reflect on p. 163 |
| Snakes and Ladders | the board game Example 6 is set in | printed in Example 6 (p. 164) and in the Did you know? box (p. 161) |
| dilution | the mechanism by which a fixed surplus becomes a negligible proportion | an added term; the chapter states the conclusion and never gives the mechanism |
| self-correcting | of a process that repays an imbalance — which a fair coin is not | an added term, introduced in section 2 only to be knocked down |
Where people slip up
- "Tails is due." Nothing is due. The chapter's box exists for this sentence alone.
- "The Law of Large Numbers guarantees the counts even out." It guarantees the proportion settles. The gap between the head count and the tail count typically grows as tosses pile up; it is the ratio that shrinks. Conflating the two is the fallacy in mathematical dress, and it is the reason this topic follows the Law of Large Numbers rather than preceding it.
- "A long run means the coin remembers." It means you saw a run. On a fair coin six heads happens about once in 64 six-toss attempts.
- "A fair coin cannot give six heads in a row." It can, with probability 1/64.
- "Independent means the two things have nothing to do with each other in subject matter." It means one result does not change the other's probabilities. Two tosses of the same coin are the same coin and are still independent.
- "'Fair' describes the person tossing." Unbiased describes the coin; random toss describes the throwing. The chapter separates them in the same box and the separation is the content.
- "After a long run, switch your prediction." Neither prediction is better. If you have reason to doubt the coin, doubt the coin — do not bet against it on the strength of six tosses.
- "This only matters for gambling." It is the reason a student misreads their own twenty-toss table in Exercise Set 7.2 Q3, and the reason people expect a monsoon to "make up" a dry week.
Ask your teacher a person
Your teacher reads this and writes back, usually within a day. For an instant answer, use Ask the video in the sidebar.
Your class sees the question and the answer. Only your teacher sees that it was you.
No questions on this topic yet.
Worked answers to this chapter’s exercises · this video explains Exercise Set 7.2 Q3
Transcript1,431 words
A fair coin has just come up heads six times running. Almost everybody feels the same thing at this point: a tail is owed. Not merely possible next time, but somehow due, as though the coin had run up a debt. The feeling is worth taking seriously rather than laughing at, because the belief underneath it is half correct. Over a great many tosses the proportion of heads really does settle near a half.
The mistake is in the mechanism: people imagine the coin repaying what it owes, and it does nothing of the kind. Before answering, ask what the coin would have to be able to do for the feeling to be right. It would need to keep a record: to know that six heads have gone by. And it would need to act on that record, leaning towards tails on the next throw to settle the account.
Now put that beside what was assumed at the start, that the coin is fair, meaning neither side is favoured on any throw. One of those says the throw depends on nothing; the other says it depends on the last six. The intuition does not add something to the fair coin, it contradicts it. So the answer is flat: the probability of tails on the next toss is still one half.
The coin has no memory, no record, and no way to act on one, and each toss starts from nothing. But stated on its own, that answer is unsatisfying, and it should be. If nothing is ever repaid, what makes the long run come out even? That question has an answer, it is arithmetic rather than assertion, and it is the whole of this video. Start from where the run left off: six heads in six tosses, so heads in every toss so far.
The running proportion of heads is one point zero. Now keep tossing, and let the coin do the ordinary thing, which is split the rest about evenly. By a hundred tosses: six heads, plus half of the remaining ninety-four, which is fifty-three out of a hundred, or nought point five three. By a thousand tosses, six plus half of nine hundred and ninety-four, which is five hundred and three out of a thousand: nought point five nought three.
By ten thousand, five thousand and three out of ten thousand: nought point five nought nought three. The proportion is walking towards a half, and it is walking there fast. Now look at what happened to the six heads themselves. In every one of those lines the heads run exactly three above half the tosses. Three above half at a hundred, three above half at a thousand, three above half at ten thousand.
Nothing was cancelled and nothing was given back. Read it as heads minus tails and the answer is even blunter: the expected lead is six after the run, and it is still six after ten thousand tosses. What changed is the number underneath it. Three out of a hundred is three per cent; three out of ten thousand is three parts in ten thousand. The surplus is not repaid, it is diluted, and dilution is what makes long-run stability happen.
One honest correction, because the surplus does sometimes disappear. A coin tossed long enough will wander back to level heads and tails at some point. Within twenty more tosses that happens about nineteen times in a hundred; within a hundred more, about fifty-five; within three hundred more, about seventy-three. But look at what is doing the work: that is chance wandering, and it wanders past level and out the other side just as readily.
It is not the coin settling an account, and you cannot use it to predict a single throw. The expected lead stays at six the whole time. Same reasoning, different object. You are playing a board game with an ordinary six-sided die and have just rolled three sixes in a row. A fourth six now feels out of the question. Count it out instead of feeling it out. Four rolls of a die can fall in one thousand two hundred and ninety-six ways, all equally likely.
Six of those ways open with three sixes, and exactly one of the six carries a fourth six. So the fourth roll is a six one time in six, which is precisely what it was before you touched the die. The three sixes were unusual, one run in two hundred and sixteen, and being unusual is not the same as being spent. The word for this is independent, and it gets used loosely, so here is a working definition.
Two trials are independent when knowing how the first came out does not change the probabilities for the second. Notice what that does not say: it does not say the two have nothing to do with each other. Two tosses of the same coin, in the same hand, in the same minute, are as connected as two events could be in every way except the one that counts, and they are still independent.
Independence is about probabilities changing, not about subjects being unrelated. Two more words, and keeping them apart is the point. A coin is fair when it is symmetrical: the two faces are alike enough that neither is favoured, so there is no physical reason for it to land one way more often. That is a claim about the object, and you could in principle check it by cutting the coin in half and looking.
Unbiased is the name for that same property. A random toss is something else: the coin drops of its own accord, with nothing and nobody steering it. That is a claim about the procedure. A perfectly symmetrical coin placed carefully on the table heads-up satisfies the first and fails the second, and you get heads every time. Here is a trap worth walking into deliberately. Imagine building the coin the fallacy wants: after every toss it leans against whichever side is ahead.
Run it, and something strange comes out. Toss by toss it is perfectly unbiased, favouring heads exactly half the time on the first toss, the second, the third, and every toss after. And yet after six heads it gives heads only one time in eight. So being unbiased and having no memory are two different properties, and a coin can have the first without the second. It is worth adding that unbiased has to mean every toss: a coin that is even on its first throw and leans three to one afterwards is not an unbiased coin.
Now the question that catches people, and it catches them because of where it sits. You toss a coin twenty times and write down every result. You work out how often heads came up, how often tails came up, the proportions, all from your own record. Then you are asked for the probability of tails on one more toss. After twenty tosses of writing down real frequencies, the tempting answer is whichever side is behind.
The answer is one half. Twenty tosses can show twenty-one different tallies, from no heads at all to all twenty, and every single one of them leaves the next toss at a half. So does the order they came in. Which leaves one honest question: is a long run evidence of anything at all? Yes, but not about the next toss. Six tosses fall sixty-four ways and exactly one is all heads, so a fair coin gives six heads about one attempt in sixty-four: unusual, nowhere near impossible.
Suppose you suspect the coin leans, favouring heads three times in four. Six heads support that coin over a fair one by seven hundred and twenty-nine to sixty-four, under twelve to one, and twelve to one is a suspicion rather than a verdict. A hundred heads would be another matter entirely. So the run tells you about the coin, and nothing about the next toss once you have decided what the coin is.
One last thing, since this is a video about not being fooled by numbers. A sixth is nought point one six six six, going on forever, so to three places it rounds to nought point one six seven, and as a percentage, sixteen point seven. Written as nought point one six six, or sixteen point six, it has been cut short rather than rounded: a tenth of a point further from the truth.
The rule underneath all of it fits in one line: nothing is owed, and the long run needs no debt to settle.
Where this fits
Taken from the notes each video was made from, not from the reading order — these are the ideas this one rests on and the ones that later rest on it.
Builds on
- Experimental probability: relative frequency over many trialsClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability
- Theoretical probability: counting favourable outcomes when all are equally likelyClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability
- What we mean by randomClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability
Comes up again in
- Tree diagrams make the sample space of a two-step experiment visibleClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability
Either side of this one
- Estimating from statistical data, and scaling the estimate upClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability
- Listing every outcome: the sample spaceClass 9 · Ch 7, The Mathematics of Maybe: Introduction to Probability