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Foundations of Probability

To reason about uncertainty, first separate the possible outcomes from the events you care about.

A coin toss produces heads or tails. We cannot know the next outcome in advance, but we do know what can happen.

Probability studies situations in which individual outcomes are uncertain but the structure of possible outcomes is known.

Key question

What can happen, which outcomes matter, and how likely are they?

1

Concept

What are trials, sample spaces, and events?

Four ideas organize a random process

Trial

One repeatable process whose outcome is uncertain

Sample space

The set of all possible outcomes

Event

A collection of outcomes we care about

Probability

A number from 0 to 1 describing how likely an event is

One roll of a die

Trial: roll the die once

Sample space: {1, 2, 3, 4, 5, 6}

Event A: an even result = {2, 4, 6}

2

Why It Matters

How is probability calculated?

For equally likely outcomes, probability is a ratio of counts

Probability of event A

outcomes in A ÷ all possible outcomes

Three of the six die outcomes are even, so the probability is 3/6 = 0.5.

ProbabilityMeaning
0Impossible
0.5An even chance
1Certain
3

How It Works

How do unions and intersections differ?

“Or” and “and” describe different events

A union contains outcomes in A or B. An intersection contains outcomes that belong to both A and B.

ExpressionNotationExample
A or BA ∪ Beven or at least 4
A and BA ∩ Beven and at least 4
not AAᶜnot even

Do not count the overlap twice

P(A ∪ B) = P(A) + P(B) − P(A ∩ B)

4

Example

Theoretical and empirical probability

Theoretical and empirical probability are not identical

A fair coin has theoretical heads probability 0.5, but ten actual tosses might produce seven heads.

The observed proportion is an empirical probability. It can vary widely in small samples and tends to settle toward the theoretical probability as the number of trials grows.

A common mistake

Five heads in a row do not make tails more likely on the next independent toss.

5

Interactive

Change the number of trials

Change the number of trials

Change the success probability and trial count to see how much the observed proportion can move.

Trials

20

Expected probability

0.50

Observed successes

7

Observed proportion

0.35

With more trials, the observed proportion usually settles closer to the theoretical probability, although random variation never disappears completely.

Key lesson

Probability describes a long-run pattern across repeated trials, not a guarantee about the next outcome.

Key takeaways

  • A trial is a repeatable process with an uncertain outcome.
  • The sample space contains every possible outcome.
  • An event is a set of outcomes of interest.
  • Probability ranges from 0 to 1.
  • Union probabilities must account for overlap.
  • Empirical probability tends to approach theoretical probability as trials accumulate.

Go Deeper

Conditional Probability & Bayes’ Theorem