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Data, probability and statistics I · Lesson 08 / 12

Probability starts with a model

A virtual bag holds four distinguishable tokens: three blue and one gold. A draw gives every token the same chance. These rules let us calculate before simulating.

Learning paths18 min

Let's discover together

  1. 1

    The four elementary outcomes are blue 1, blue 2, blue 3 and gold 1. “Blue” is an event containing three outcomes. With equal chances, P(blue) = 3/4 = 75%; P(gold) = 1/4 = 25%.

  2. 2

    Do not count just the two colors as if they had equal chances. Counting favorable/possible outcomes works this way when elementary outcomes are equally likely. A physical bag may require checking whether its mechanism fits the model.

  3. 3

    In this simulation, the token returns before each draw. The composition stays three out of four and the modeled draws are independent. A run of gold tokens does not create a debt requiring the next one to be blue.

  4. 4

    Without replacement, after removing a blue token there are two blue among three tokens: the next chance changes to 2/3. State the rules before multiplying probabilities; do not assume independence just because there are two events.

Evidence lab

Experiment with fictional deliveries at Aurora station. Analysis runs in this browser. Nothing entered here is sent to the server.

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Header id,group,minutes; one delivery per row; unique id; groups A/B; decimal point; duration 0–1440 with up to two decimal places. Blank means unknown. Up to 100 rows. No quotes or decimal commas. Use invented data.

Selection uses the entered table, including missing values. A draw does not guarantee representativeness. The same seed reproduces selection; it cannot recover cases outside the table.

Make a prediction and analyze the table.

Virtual bag: model with replacement

There are four equally likely tokens; choose how many are blue. Each token returns before the next draw. This is a deterministic simulation, without prizes, money or real observations.

Repeating the seed repeats the sequence. More draws do not guarantee a closer result in every run and do not validate the real-world model.

Two rules, the same accuracy

Ten fictional cases: nine normal and one failure. Rules defined for this example; no AI training or real equipment testing. Same number correct, different consequences.

Two rules, the same accuracy
RuleCorrect / totalMissed failuresFalse alarms

Your investigation report

Official links and next step

✦
YOUR MISSION

Build an outcome space and distinguish replacement from dependence.

Now it's your turn

Discovery workshop

Predict, check, explain

Build an outcome space and distinguish replacement from dependence.
Build your answers and check when you are ready.
A moment to understand

Why is blue not a 50% chance in this bag?

I need a hint

Probability is conditional on a model. Changing the composition or replacement rule changes the question.

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Take it with you

Draw five tokens, two blue. Calculate the initial chance and the blue chance after removing one blue without replacement. Explain why they differ without needing a real draw.

At your own pace

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