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Module 1 · Lesson 2

Probability: Insurance, genetics, and random algorithms, and the open questions of chance

You'll see probability at work in insurance, in the inheritance of traits, and in computers that use randomness to solve problems and to write text. You'll also see why mathematicians and philosophers still disagree about what a probability actually is.

What you will be able to do

  • Explain how probability is used in insurance, genetics, and computing, and describe one open question about chance.

0% of this lesson · 10 items · 1h 33m total · 1h 18m without the optional activity

Contents of this lesson10 items
  1. ReadingYour Insurance Premium Is an Expected Value3 min
  2. ReadingInsurance and Life Tables: Pooling Risk4 min
  3. ReadingProbability in Genetics: Mendel's Ratios4 min
  4. ReadingRandomness as a Tool: Monte Carlo Methods and AI Text Generation4 min
  5. ReadingWhat Is a Probability? Frequency, Belief, and an Unsettled Debate4 min
  6. Guided ReadingGuided Walkthrough: Predicting Pea Colors with a Punnett Square7 min
  7. Guided ConversationWhat Does "70 Percent" Mean to You?12 min
  8. Hands-on Activity · optionalWatch Chance Settle Down15 min
  9. Knowledge CheckInsurance, genetics, and random algorithms, and the open questions of chance10 min
  10. Graded QuizProbability30 min

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Reading 3 min Done

Your Insurance Premium Is an Expected Value

If you pay for car, home, health, or life insurance, you pay a premium: a fixed amount, every month or year, whether or not anything goes wrong. Most years you claim nothing. In a bad year, the insurer may pay you far more than you paid in. How can a company set a price for something as unpredictable as your next accident?

The answer is that the insurer isn't trying to predict your next accident. It can't know whether you'll file a claim this year, and it doesn't need to. It needs to know how many claims to expect across all its customers together.

Those two questions behave very differently. For one person, the result is all or nothing: no claim, or a large one. For a hundred thousand people, the total is surprisingly steady from year to year. Individual surprises pile up into a predictable sum.

That steadiness is the law of large numbers at work. The law says that when an experiment is repeated many times under similar conditions, the fraction of times an event happens settles toward its probability. If each policy has a small, independent chance of a claim, then across many policies the fraction that claim settles close to that chance. The insurer can plan on the average, not the individual.

The idea has a long history. In 1693 the astronomer Edmond Halley published mortality tables for the city of Breslau. MacTutor describes them as one of the earliest works to relate mortality and age in a population, and as highly influential on the later actuarial tables used in life insurance (O'Connor and Robertson 2000). Once you know roughly what fraction of people at each age die in a year, you can work out what a life insurance policy is likely to cost the company that sells it.

Your premium is built on that logic. In outline:

  • Estimate the chance of each kind of claim, and its size
  • Multiply each claim size by its chance, and add, to get the expected payout per policy
  • Add the insurer's costs of running the business

Actuaries, the specialists who price insurance, state the first principle directly: a rate is an estimate of the expected value of future costs. The same statement lists those costs as claims, the expense of settling them, the costs of running the company, and the cost of capital (Casualty Actuarial Society 1988).

So your premium is an expected value with costs added. Nobody knows what your year will bring. Across everyone who buys the same policy, the average is close enough to known that a company can promise to pay.

What the branch offers here is a way to turn individual uncertainty into collective planning. You trade a small, certain cost for protection against a large, uncertain one, and the insurer can make that trade because it holds many such risks at once.

This content reflects the field as of October 2026.

References

  • Casualty Actuarial Society. 1988. Statement of Principles Regarding Property and Casualty Insurance Ratemaking. Adopted May 1988; reinstated May 2021.
  • O'Connor, J. J., and E. F. Robertson. 2000. "Edmond Halley." MacTutor History of Mathematics Archive, University of St Andrews. Last updated January 2000.

Reading 4 min Done

Insurance and Life Tables: Pooling Risk

Introduction

An insurance company sells promises to pay for losses it can't foresee. It stays solvent because, across many customers, the total of those losses is far more predictable than any single one.

This reading covers the life tables that started the mathematics of insurance, how a premium is built from expected values, why pooling works, and when it fails.

Life Tables

A life table is a table that shows, for a population, how many people at each age are expected to die within a year, or what fraction survive to each age. With one, you can estimate the chance that a person of a given age will die in the coming year.

In 1693 the astronomer Edmond Halley published mortality tables for the city of Breslau, now Wrocław in Poland. MacTutor describes them as one of the earliest works to relate mortality and age in a population, and as highly influential on the actuarial tables later used in life insurance (O'Connor and Robertson 2000). The key step was treating death, unpredictable for any one person, as a pattern across a population.

From Expected Payout to Premium

A premium is the price a customer pays for an insurance policy. It starts from the expected payout: the average amount the insurer expects to pay on a policy, found by multiplying each possible claim by its probability and adding.

Here is a simplified example with made-up numbers. Suppose a one-year life insurance policy pays $100,000 if the policyholder dies during the year, and the life table gives a person of that age a chance of 1/1000 of dying that year. The expected payout is

100{,}000 \times \frac{1}{1000} = 100

or $100 per policy. The premium must cover that $100 plus the insurer's costs. The Casualty Actuarial Society's statement of ratemaking principles puts the first principle this way: a rate is an estimate of the expected value of future costs. The statement counts claims, the cost of settling them, operating expenses, and the cost of capital among those costs (Casualty Actuarial Society 1988).

Why Pooling Works

For one policyholder, the insurer's result is all or nothing. It pays $0 or $100,000. Charging $100 makes no sense for a single policy on its own.

Risk pooling is combining many similar, independent risks so that the total loss becomes predictable. Suppose the insurer sells the same policy to 10,000 people whose risks are independent, meaning one person's death doesn't change anyone else's chance. The expected number of deaths is 10{,}000 \times \frac{1}{1000} = 10. The actual number will vary, but the law of large numbers keeps it near that figure. That law says that over many independent trials, the fraction of times an event happens settles toward its probability. In this example, the number of deaths falls between 4 and 16 about 96 percent of the time.

Number of policiesPossible resultsWhat the insurer can plan on
1Pay $0 or $100,000Almost nothing
10,000Usually 4 to 16 claims, around 10Total claims of about $1 million, give or take

The larger the pool, the smaller the swings in the claim rate. That's why insurers want many customers with similar, independent risks.

When Pooling Fails

Pooling depends on independence. A correlated risk is a risk where one loss makes other losses more likely, so many claims tend to arrive together. A flood that damages thousands of homes in the same region, or a pandemic that raises deaths across a whole population at once, doesn't average out. The claims don't arrive as scattered, separate events. They arrive together.

When risks are correlated, the law of large numbers no longer protects the insurer in the same way. Adding more customers in the same flood zone adds more exposure to the same event, not more independent chances that cancel. This is why correlated risks are hard to insure and why insurers watch how their policies are concentrated by place and by type of risk.

Conclusion

Insurance turns many uncertain individual losses into a fairly predictable total. Life tables supply the probabilities, expected values set the starting price, and the law of large numbers makes the pool stable. The method holds as long as risks are close to independent, and it strains when one event can hit many policies at once.

This content reflects the field as of October 2026.

Key Terms

  • Life table: A table that shows, for a population, how many people at each age are expected to die within a year, or what fraction survive to each age.
  • Premium: The price a customer pays for an insurance policy.
  • Expected payout: The average amount an insurer expects to pay on a policy, found by multiplying each possible claim by its probability and adding.
  • Risk pooling: Combining many similar, independent risks so that the total loss becomes predictable.
  • Correlated risk: A risk where one loss makes other losses more likely, so many claims tend to arrive together.

References

  • Casualty Actuarial Society. 1988. Statement of Principles Regarding Property and Casualty Insurance Ratemaking. Adopted May 1988; reinstated May 2021.
  • O'Connor, J. J., and E. F. Robertson. 2000. "Edmond Halley." MacTutor History of Mathematics Archive, University of St Andrews. Last updated January 2000.

Reading 4 min Done

Probability in Genetics: Mendel's Ratios

Introduction

Two pea plants with yellow seeds can produce offspring with green seeds. A trait can skip a generation and come back. Gregor Mendel showed that this pattern follows the rules of chance.

This reading covers Mendel's pea experiments, the probability model behind them, the 3-to-1 ratio, and why real counts match it only approximately. Citations name Mendel's numbered experiments.

Mendel's Experiments

Gregor Mendel bred pea plants and tracked traits that come in two distinct forms. He studied seven such traits, including whether the ripe seeds are round or wrinkled and whether the seed's interior is yellow or green (Mendel 1925). He presented his results in Brünn (now Brno) in 1865, and they were published in 1866. An English translation made for the Royal Horticultural Society appeared in 1901.

Mendel crossed plants that bred true for each form, such as a yellow-seeded line with a green-seeded line. The hybrids all showed one form, yellow. Mendel called the form that appears in the hybrid dominant and the form that disappears, or becomes latent, recessive (Mendel 1925). When he let the hybrids fertilize themselves, the green form came back in some of the next generation.

The Probability Model

Mendel didn't use the word "gene," which came later. In today's terms, a gene is a unit of heredity, passed from parent to offspring, that influences a trait. The model works like this:

  • Each plant carries two versions of the gene for seed color, one from each parent.
  • A hybrid carries one yellow version and one green version. It looks yellow because yellow is dominant.
  • When a hybrid reproduces, each offspring gets one version from each parent, and each version is equally likely to be the one passed on, with probability 1/2.

Now cross two hybrids. Write Y for the yellow version and y for the green. Each parent passes Y or y with probability 1/2, independently. A Punnett square is a grid that lists the versions one parent can pass on along the top and the other parent's down the side, with each cell showing one equally likely offspring combination.

Yy
YYYYy
yyYyy

Each of the four cells has probability \frac{1}{2} \times \frac{1}{2} = \frac{1}{4}. Three cells contain at least one Y and give yellow seeds. Only yy gives green. So the expected ratio, a comparison of two quantities by division, is 3 yellow to 1 green. Mendel's own notation shows the underlying 1 to 2 to 1 pattern of pure yellow, mixed, and pure green.

Counts Versus Ratios

Mendel's totals came close to 3 to 1. In his seed-color experiment, 258 plants yielded 8,023 seeds: 6,022 yellow and 2,001 green, which he reported as a ratio of 3.01 to 1. In his seed-shape experiment, he counted 5,474 round seeds and 1,850 wrinkled, a ratio of 2.96 to 1 (Mendel 1925, Expts. 1 and 2).

Single plants were much less tidy. Mendel reported one plant with 32 yellow seeds and only 1 green, and another with 20 yellow and 19 green. He noted that such counts show how large the fluctuations can be when numbers are small (Mendel 1925, Expt. 2). This is what the law of large numbers predicts: over many independent trials, the fraction of times an outcome happens settles toward its probability. A small sample can wander far from 3 to 1, while thousands of seeds land close to it.

SampleYellowGreenRatio
One plant32132 to 1
Another plant2019about 1.05 to 1
All 258 plants6,0222,0013.01 to 1

The 3-to-1 ratio applies to traits like Mendel's, controlled by one gene with a dominant and a recessive form. Many traits depend on several genes and on the environment, and they don't follow simple ratios.

Conclusion

Mendel's ratios rest on a probability model: each parent passes one of two versions of a gene, each with probability 1/2. Crossing two carriers gives four equally likely combinations and an expected 3-to-1 ratio. Real counts match that ratio closely only when samples are large.

This content reflects the field as of October 2026.

Key Terms

  • Dominant: Describes the form of a trait that appears in a hybrid that carries both forms, such as yellow seeds in Mendel's peas.
  • Recessive: Describes the form of a trait that disappears, or becomes latent, in a hybrid that carries both forms, such as green seeds in Mendel's peas.
  • Gene: A unit of heredity, passed from parent to offspring, that influences a trait.
  • Punnett square: A grid that lists the versions one parent can pass on along the top and the other parent's down the side, with each cell showing one equally likely offspring combination.
  • Ratio: A comparison of two quantities by division, such as 3 yellow seeds to 1 green.

References

  • Mendel, Gregor. 1925. Experiments in Plant-Hybridisation. Translated for the Royal Horticultural Society, London (1901); with William Bateson. Cambridge, MA: Harvard University Press.

Reading 4 min Done

Randomness as a Tool: Monte Carlo Methods and AI Text Generation

Introduction

Randomness usually sounds like the enemy of calculation. Yet some of the most useful computer methods add randomness on purpose, and so do the AI systems that write text.

This reading covers the Monte Carlo method, a simple example of it, how AI language models use probability to choose words, and why randomness helps.

The Monte Carlo Method

In 1949 Nicholas Metropolis and Stanislaw Ulam published a paper titled "The Monte Carlo Method" in the Journal of the American Statistical Association. They described it as a statistical approach to problems in mathematical physics, built on sampling rather than exact calculation (Metropolis and Ulam 1949).

Their first example was a card game. Working out the exact chance of winning a game of solitaire by counting every arrangement of the cards is, in their words, "a completely intractable task." A practical alternative is to play many games and count how often you win. The fraction of wins estimates the probability (Metropolis and Ulam 1949).

That's the core idea. The Monte Carlo method is a way of estimating a quantity by running many random trials and averaging the results, instead of calculating the quantity directly. It relies on random sampling, choosing cases by chance so that each possible case has a known chance of being picked. It also relies on the law of large numbers: as the number of trials grows, the fraction of successes settles toward the true probability. Metropolis and Ulam noted that computing machines were well suited to running such trials, even many machines working in parallel.

A Simple Case: Estimating an Area

Here is a small version you could run on any computer. Draw a square with sides of length 1, and inside it draw a quarter of a circle with radius 1, centered on one corner. The quarter circle covers a fraction of the square equal to \pi/4, about 0.785. The symbol \pi (pi) is the familiar circle constant, about 3.14.

Now scatter points at random across the square, so that every spot is equally likely. Count the fraction that land inside the quarter circle. That fraction estimates the area. With 1,000 points, the estimate is typically off by around 0.013. With a million points, the typical error falls to about 0.0004. More points mean a better estimate, at a cost in computing time.

Each run of this experiment is a simulation: a computer imitation of a random process, run to see what the process tends to produce. Metropolis and Ulam described the same counting idea for a region in a space of twenty dimensions, where no picture or direct formula would help (Metropolis and Ulam 1949).

How AI Language Models Choose Words

AI language models, the systems behind chatbots, also rely on probability, though in a different way. Stephen Wolfram's 2023 explanation of ChatGPT describes the basic step. Given the text so far, the model produces "a ranked list of words that might follow," each with a probability (Wolfram 2023). Strictly, the models work with tokens, which can be whole words or pieces of words.

That list is a probability distribution: a list of possible outcomes together with the probability of each, where the probabilities add up to 1. The model then picks the next word from it and repeats the process, word after word.

Why not always pick the most likely word? Wolfram reports that doing so tends to produce flat, repetitive text. Picking lower-ranked words some of the time, at random, gives more interesting writing, and it means the same prompt can produce different responses each time (Wolfram 2023).

A setting called temperature controls how often the model picks less likely words: low temperature sticks close to the top choices, and higher temperature spreads the picks more widely. Wolfram reports that for essay writing, a temperature of 0.8 "seems best," a value found by trial rather than derived from theory (Wolfram 2023).

MethodWhat is randomWhat the randomness is for
Monte Carlo estimatePoints or trials chosen by chanceEstimating a quantity too hard to calculate directly
AI text generationThe next word, drawn from a probability distributionProducing varied, less repetitive text

Why Randomness Helps

In both cases, randomness lets a computer explore a huge space of possibilities without listing them all. Solitaire has too many card orders to count. A language model has many possible next words at every step. Random sampling looks at a manageable number of cases, chosen so that the results are fair on average.

Conclusion

Monte Carlo methods and AI text generation both turn chance into a tool. Monte Carlo methods estimate quantities by averaging many random trials. AI language models write by repeatedly sampling words from a probability distribution, with temperature setting how adventurous the choices are.

This content reflects the field as of October 2026.

Key Terms

  • Monte Carlo method: A way of estimating a quantity by running many random trials and averaging the results, instead of calculating the quantity directly.
  • Random sampling: Choosing cases by chance so that each possible case has a known chance of being picked.
  • Simulation: A computer imitation of a random process, run to see what the process tends to produce.
  • Probability distribution: A list of possible outcomes together with the probability of each, where the probabilities add up to 1.
  • Temperature: A setting in AI language models that controls how often less likely words are picked.

References

  • Metropolis, Nicholas, and S. Ulam. 1949. "The Monte Carlo Method." Journal of the American Statistical Association 44 (247): 335–341.
    • Free: None confirmed
  • Wolfram, Stephen. 2023. "What Is ChatGPT Doing … and Why Does It Work?" Stephen Wolfram Writings, February 14, 2023.

Reading 4 min Done

What Is a Probability? Frequency, Belief, and an Unsettled Debate

Introduction

A forecast says there's a 70 percent chance of rain tomorrow. Everyone uses the number, but it's surprisingly hard to say what it means. Tomorrow will happen only once, and it will either rain or not.

This reading covers two main answers to the question of what a probability is, several other positions, and why the disagreement still matters in philosophy and in statistics.

Shared Rules, Disputed Meaning

Most work in probability uses the same formal rules, usually the axioms Andrey Kolmogorov set out in 1933. Philosopher Alan Hájek calls this "the usual formal theory." The rules tell you how probabilities combine. They don't tell you what a probability is about, and that question has several competing answers (Hájek 2023).

The Frequency View

On the frequency interpretation, a probability is the fraction of times an event happens in a long run of similar trials. To say a fair coin has a probability of 1/2 of heads is to say that, over many tosses, about half come up heads. The view is associated with John Venn, Hans Reichenbach, and Richard von Mises (Hájek 2023).

The view fits coins, dice, and insurance tables well. It struggles with single events. Hájek calls this "the problem of the single case." A coin tossed only once has a frequency of heads of either 0 or 1, whatever the coin is like (Hájek 2023). Von Mises accepted the consequence and regarded probabilities of single cases as meaningless.

A frequency reading of the rain forecast is still possible. The 70 percent could mean that on days like this one, it rains about 70 percent of the time. But which days count as "like this one"? Hájek calls this the reference class problem: the same event can be placed in different groups, and the groups can give different frequencies (Hájek 2023).

The Degree-of-Belief View

On the subjective probability view, a probability measures how strongly a reasonable person believes something, on a scale from 0 to 1. Hájek describes these probabilities as "degrees of confidence" or partial beliefs. The view is associated with Frank Ramsey, Bruno de Finetti, and Leonard Savage (Hájek 2023).

This view has no trouble with tomorrow's rain. The 70 percent expresses how confident someone should be, given the evidence. It also comes with a rule for learning: when new evidence arrives, update your probabilities by conditioning on it, that is, by moving to the probability of each claim given the evidence (Hájek 2023). Critics ask whose beliefs count, and whether this makes probability too personal to be scientific.

Other Positions

Hájek's survey describes further interpretations alongside these two (Hájek 2023):

InterpretationA probability is…
ClassicalThe share of equally possible cases, assigned when the evidence is balanced among them
LogicalThe degree to which evidence supports a claim
FrequencyThe fraction of times an event happens in a long run of trials
SubjectiveA reasonable person's degree of belief
PropensityA physical tendency of a setup, such as a die, to produce an outcome

The propensity view, often credited to Karl Popper as its pioneer, tries to keep probability objective while applying it to single cases. Each position handles some uses of probability better than others, and none has won general agreement.

Why It Matters in Statistics

The debate isn't only philosophical. Statistics has two broad families of methods. Frequentist statistics treats probabilities as long-run frequencies of events. Bayesian statistics treats probabilities as degrees of belief that are updated with evidence. Philosopher Jan-Willem Romeijn notes that the match between the interpretations and the methods is "far from exact," but the link is real, and the debates over which methods are valid are ongoing (Romeijn 2025).

Conclusion

Probability's rules are widely shared, but its meaning is not. Frequency views tie probability to what happens over many trials, and degree-of-belief views tie it to reasonable confidence. Philosophers and statisticians continue to argue over which fits which uses, and the question remains open.

This content reflects the field as of October 2026.

Key Terms

  • Frequency interpretation: The view that a probability is the fraction of times an event happens in a long run of similar trials.
  • Subjective probability: The view that a probability measures how strongly a reasonable person believes something, on a scale from 0 to 1.
  • Frequentist: Describes statistical methods that treat probabilities as long-run frequencies of events.
  • Bayesian: Describes statistical methods that treat probabilities as degrees of belief that are updated with evidence.

References

  • Hájek, Alan. 2023. "Interpretations of Probability." Stanford Encyclopedia of Philosophy. First published 2002; substantive revision November 16, 2023.
  • Romeijn, Jan-Willem. 2025. "Philosophy of Statistics." Stanford Encyclopedia of Philosophy. First published 2014; substantive revision October 1, 2025.

Guided Reading 7 min Done

Guided Walkthrough: Predicting Pea Colors with a Punnett Square

Introduction

Gregor Mendel's pea experiments, published in 1866, showed that heredity follows the rules of probability (Mendel 1925). A short table, now called a Punnett square, turns his idea into a prediction you can check against his counts.

This walkthrough builds the table for one cross, predicts counts for small and large batches of offspring, and compares the prediction with numbers Mendel reported.

The Starting Point

You have two pea plants. Each carries one version of the gene for yellow seeds and one version for green seeds. A gene is a unit of heredity, passed from parent to offspring, that influences a trait. Mendel didn't use that word, which came later, but the idea matches his model.

Yellow is dominant: a plant with one yellow version and one green version has yellow seeds. Green is recessive: it shows only when a plant carries two green versions. So both parent plants look yellow, even though each carries a hidden green version.

Write Y for the yellow version and y for the green. Each parent is Yy. The question is what colors their offspring will have, and in what proportions. In peas, each seed is a new offspring, so you can count offspring by counting seeds.

Walking Through the Cross

Step 1: List what each parent can pass on

Each offspring receives one version of the gene from each parent. A Yy parent passes on either Y or y, and the two are equally likely. So each parent passes Y with probability 1/2 and y with probability 1/2.

The two parents act independently. What one parent passes on doesn't change what the other passes on. That independence is what lets you multiply probabilities in the next step.

Step 2: Build the four-cell table

Put one parent's possible versions across the top and the other parent's down the side. Each cell combines one version from each parent.

Y (from parent 1)y (from parent 1)
Y (from parent 2)YYYy
y (from parent 2)Yyyy

Each cell has a probability of \frac{1}{2} \times \frac{1}{2} = \frac{1}{4}. The four cells are equally likely, and together they cover every possibility, so their probabilities add up to 1.

Step 3: Count the colors

Read the table cell by cell:

  • YY: one cell, pure yellow
  • Yy: two cells, mixed, which look yellow because yellow is dominant
  • yy: one cell, pure green

The genetic types come in the pattern 1 to 2 to 1. Mendel's own formula for the offspring of hybrids, written A + 2Aa + a, records the same pattern (Mendel 1925). The colors, though, come in the pattern 3 to 1, because three of the four cells contain at least one Y. So the chance that an offspring has yellow seeds is 3/4, and the chance that it has green seeds is 1/4.

Step 4: Predict counts for 100 and for 8,000 offspring

Expected counts come from multiplying the number of offspring by each probability.

OffspringExpected yellowExpected green
100100 \times \frac{3}{4} = 75100 \times \frac{1}{4} = 25
8,0008{,}000 \times \frac{3}{4} = 6{,}0008{,}000 \times \frac{1}{4} = 2{,}000

Which batch is likelier to land near 3 to 1? The law of large numbers says that as the number of trials grows, the fraction of times an outcome happens settles toward its probability. So the larger batch should come closer.

The numbers bear this out. Take "near 3 to 1" to mean that between 73 and 77 percent of offspring are yellow. A batch of 100 lands in that window only about 44 percent of the time. A batch of 8,000 lands in it almost every time, with a chance above 99.99 percent. The count of yellow offspring varies more in absolute terms in the larger batch, but as a fraction of the batch it varies far less.

Step 5: Compare with Mendel's counts

In his experiment on seed color, Mendel let hybrids of exactly this kind fertilize themselves, which gives the same four-cell table. He reported that 258 plants yielded 8,023 seeds, of which 6,022 were yellow and 2,001 green, a ratio he gave as 3.01 to 1 (Mendel 1925, Expt. 2).

The prediction for 8,023 seeds is 8{,}023 \times \frac{3}{4} \approx 6{,}017 yellow and about 2,006 green. Mendel's count is off by about 5 seeds in each color. His yellow share was about 75.06 percent, against a predicted 75 percent.

Single plants told a different story. Mendel reported one plant with 32 yellow seeds and 1 green, and another with 20 yellow and 19 green (Mendel 1925, Expt. 2). Each is a small sample, and each is far from 3 to 1. The ratio emerges in the total, not in every pod.

Key Considerations

Terms then and now. Mendel wrote of "differentiating characters," not genes, and the English translation keeps his terms "dominant" and "recessive." He described the seed color as the color of the seed albumen. The table works the same in either vocabulary.

The conditions the prediction needs. The 3-to-1 prediction assumes that each parent passes each version with probability 1/2, that the parents act independently, and that the trait depends on one gene with a dominant and a recessive form. Mendel chose traits that come in two distinct forms. Many traits don't work this way, and they don't follow simple ratios.

The common mistake: expecting exactly 3 to 1 in a small sample. A ratio of 3 to 1 means each offspring has a 3/4 chance of yellow. It doesn't mean that every group of four offspring has exactly three yellow and one green. With four offspring, all four yellow is quite possible, and so is two and two. Mendel's plant with 20 yellow and 19 green seeds isn't evidence against the model. It's what you should expect to see sometimes in a sample that small. The ratio is a statement about the long run, and only large totals should be expected to land close to it.

Summary

Crossing two carriers gives four equally likely combinations, three of which show the dominant color, so the model predicts 3 yellow to 1 green. Mendel's large total matched that prediction closely, and his single plants did not.

Yy
YYY (yellow)Yy (yellow)
yYy (yellow)yy (green)
SamplePredicted yellow : greenObserved yellow : green
8,023 seeds (258 plants)about 6,017 : 2,0066,022 : 2,001 (3.01 to 1)
One plantabout 3 to 132 : 1
Another plantabout 3 to 120 : 19
  1. Each cell of the table has probability 1/4, and three cells give yellow, so the chance of yellow is 3/4.
  2. The large total lands within about 5 seeds of the prediction, as the law of large numbers leads you to expect.
  3. Single plants vary widely, so a small sample far from 3 to 1 doesn't contradict the model.

References

  • Mendel, Gregor. 1925. Experiments in Plant-Hybridisation. Translated for the Royal Horticultural Society, London (1901); with William Bateson. Cambridge, MA: Harvard University Press.

Guided Conversation 12 min Done

What Does "70 Percent" Mean to You?

In this conversation, you'll start from a probability you've met recently, such as a weather forecast, a risk figure, or a game, and ask what that number really means. You'll connect it to how insurance, genetics, or AI tools depend on chance behaving predictably in bulk. You'll leave with your own view of which interpretation of probability fits your example best.

You'll have this conversation with an AI assistant, using your own account. Choose a button to open a new chat with the prompt already filled in, then press send to start. If the chat opens empty, copy the prompt and paste it in.

Run this conversation in whichever assistant you already use:

Claude desktop app

To use another LLM, simply copy and paste the prompt into its chat window.

Show the full prompt (it lists misreadings to watch for, so skip it if you would rather come to the conversation fresh)
Guided Conversation: What Does "70 Percent" Mean to You? (about 12 minutes)

Note to the learner: press send to start. Everything below is facilitator guidance for the AI. It lists misconceptions to watch for, so skip it if you'd rather come to the conversation fresh.

Please facilitate a reflective dialogue with me. I'm an adult who is comfortable with basic high-school algebra but may be rusty or uneasy with math, exploring what probability is and whether to study it further. I'm studying how probability is used in insurance, genetics, and computing, and the open question of what a probability means. Follow this guidance for the whole conversation.

GOAL
Help me explain how probability is used in insurance, genetics, or computing, and describe the open question of what a probability means, starting from a probability I've met in my own life.

HOW TO RUN THE CONVERSATION
- Ask one question at a time, then wait for my reply. Keep each of your turns under about 120 words.
- Don't lecture. Explain a point only when I need it to continue, then return to my example.
- Be curious, collegial, and patient. Define terms briefly on first use. Welcome disagreement when I give a reason. Write any math in plain text, such as 3/4.
- Plain conversation only: don't search the web or create files or documents.
- Aim for about 12 minutes, mostly on topics 2 and 3. If my replies are brief, offer one concrete prompt, such as "a 70 percent chance of rain tomorrow," and move on. If I seem uncertain, shorten the conversation to 5-7 minutes. Always reach the final topic.
- Start now. Open with one or two warm sentences: this is a conversation, not a quiz; my own reasoning matters more than a textbook answer; I can ask you to clarify anything. Then ask me for a probability I've met recently: a weather forecast, a risk figure in the news, a game, or an AI tool's output.

TOPICS, IN ORDER
1. My example. Ask what the number was and where I saw it. Follow up: what did I take it to mean at the time?
2. Frequency or belief? Explain briefly: on the frequency view, a probability is the fraction of times something happens over many similar trials; on the degree-of-belief (subjective) view, it measures how confident a reasonable person should be given the evidence. Ask which fits my example. Follow up: if the event happens only once, what would the "many similar trials" be?
3. Chance in bulk. Ask me to pick insurance, genetics, or AI text generation and say how it depends on probability behaving predictably over many cases. Draw out the law of large numbers: over many independent trials, the fraction of times an event happens settles toward its probability. Follow up: what would break the pattern?
4. Closing. Ask which interpretation fits my example best, and why, in one or two sentences. This is something I can take into estimating a probability by repeated trials.

KEY POINTS TO KEEP ACCURATE
- Insurers rely on pooling many independent risks, so total claims become predictable; they don't predict which customer will claim. Correlated risks, such as a flood hitting a whole region, weaken pooling.
- Crossing two carriers of a dominant and a recessive version of one gene gives four equally likely combinations and an expected ratio of 3 to 1. Real counts match it only approximately, more closely in large samples.
- AI language models produce a ranked list of possible next words with probabilities and usually sample from it with some randomness; a setting called temperature controls how often less likely words are chosen.
- Interpretations of probability are contested among philosophers and statisticians. The frequency view struggles with single events; the degree-of-belief view faces questions about whose beliefs count. Other positions include classical, logical, and propensity views.

MISCONCEPTIONS TO CORRECT GENTLY
When one appears, name the accurate version briefly, then return to my example.
- "Insurance companies predict who will claim": they predict totals across many customers.
- "A 3-to-1 ratio means exactly 3 of every 4": it holds only approximately, and more closely in large samples.
- "AI picks the single most likely word": it usually samples with some randomness, so the same prompt can give different responses.
- "A 70 percent forecast was wrong because it didn't rain": a single outcome can't confirm or refute a probability.

LIMITS
- Give no health, insurance, or financial advice, and don't interpret any risk figure for my own decisions.
- Don't take a side in the interpretation debate; present each view fairly and let me choose.

TO FINISH
After I say which interpretation fits my example, close in one short turn:
- Affirm one specific thing I worked out, in my own words where possible.
- Suggest one or two next steps that fit how the conversation went. Possible steps: test a probability by repeating an experiment many times; look at how a weather service defines its rain probabilities; compare the frequency and degree-of-belief views on a second example; review why pooling fails for correlated risks.
- Restate my view on its own line, labeled "My view", so I can copy it.

Hands-on Activity 15 minOptional Done

Watch Chance Settle Down

Overview

This activity lets you see the law of large numbers with your own dice. That law says that over many trials, the fraction of times an event happens settles toward its probability. You'll roll two dice 60 times, track how often the sum is 7, and watch the running fraction move toward its probability of 1/6. It's optional, and the results are yours to keep.

What You'll Need

  • Two dice, or a free online dice roller
  • Paper or a spreadsheet

Your Task

Roll two dice 60 times, record each sum, and track the fraction of rolls so far that gave a 7. Compare that running fraction with 1/6, about 0.167, and describe how it behaved early and late.

Steps

  1. Roll two dice 60 times and record each sum. Write the sums in a list or a spreadsheet column, numbered 1 to 60. Record every roll as it comes, without skipping any.
  2. After every 10 rolls, compute the fraction of 7s so far. After 10 rolls, divide the number of 7s by 10. After 20 rolls, divide the total number of 7s so far by 20, and so on up to 60. You'll have six running fractions.
  3. Plot or list the running fraction alongside 1/6. Make a small table with columns for rolls so far, 7s so far, running fraction, and 0.167. If you're using a spreadsheet, a line chart of the running fraction works well.
  4. Pool your results with a second set of 60 rolls, if you can. Roll another 60 times, or ask someone else for their 60, and compute the fraction of 7s over all 120 rolls. Compare it with the fraction from your first 60.
  5. Write two sentences on how the fraction behaved early and late. Say how far from 0.167 your fraction was after 10 rolls, and how far it was after 60 or 120.

What to Expect

Over 60 rolls, you'd expect about 10 sevens, because 60 \times \frac{1}{6} = 10. Your actual count will probably differ. About 95 percent of the time, 60 rolls give between 5 and 15 sevens, a fraction between about 0.08 and 0.25.

Early fractions jump around. After only 10 rolls, getting no 7s at all happens about 16 percent of the time, which gives a fraction of 0. Getting four or more 7s, a fraction of 0.4 or more, happens about 7 percent of the time. By 60 rolls, the fraction usually sits much closer to 0.167, though it rarely lands on it exactly. Sixty rolls is still a small number, so a fraction of 0.12 or 0.2 is normal.

If your fraction didn't settle, that's possible too. The law of large numbers is about very many trials, and 60 is a modest start.

Self-Check

When you're done, check that:

  • You recorded all 60 sums
  • You computed the running fraction after each block of 10 rolls
  • Your early fractions varied more than your later ones, or you can say why yours didn't
  • You didn't "correct" streaks by hand, such as rerolling or skipping rolls to bring the fraction closer to 1/6

Nothing is uploaded. Write in your own notebook or document and keep it.

Knowledge Check 10 min Done

Insurance, genetics, and random algorithms, and the open questions of chance

This ungraded knowledge check assesses your understanding of how probability works in the world and what remains unsettled about it. You'll be asked about insurance and risk pooling, Mendel's ratios, Monte Carlo methods and AI text generation, and interpretations of probability.

Note: Use this to test yourself, review the feedback on any questions you miss, and retry until you feel confident before moving forward.

5 questions · ungraded · retry as often as you like

Graded Quiz 30 min Done

Probability

This graded quiz assesses your understanding of how probability puts numbers on chance and where it is used. You'll be asked about the history of probability, sample spaces and counting, independence and expected value, the law of large numbers, insurance, Mendelian genetics, Monte Carlo methods and AI sampling, and interpretations of probability.

Note: Aim for a score of 80 percent or higher. If you score lower, use the feedback to review the topics you missed, then retake the quiz.

10 questions · target score 80% · 3 forms, rotated on each attempt