Simple Event Probabilities — I can compute probabilities of...

SECTION 1

Historical Context & Motivation

Humans have been making bets and playing games of chance for thousands of years, yet it took surprisingly long for anyone to develop a rigorous way to measure how likely an outcome really is. Ancient civilizations rolled dice made from animal bones, but they relied on superstition rather than mathematics to predict results. The formal study of probability only began in the 1600s, when gamblers turned to mathematicians for an edge at the card table.

1654

The Pascal–Fermat Correspondence

Blaise Pascal and Pierre de Fermat exchanged letters about the problem of points — how to fairly split a pot in an interrupted game — laying the mathematical foundations of probability.

1713

Bernoulli's Ars Conjectandi

Jacob Bernoulli published Ars Conjectandi, which introduced the Law of Large Numbers and formalized counting outcomes in a sample space.

1812

Laplace's Classical Definition

Pierre-Simon Laplace defined probability as the ratio of favorable outcomes to total equally likely outcomes, giving us the formula still used in classrooms today.

1933

Kolmogorov's Axioms

Andrey Kolmogorov published a set of axioms that placed probability on a rigorous mathematical footing, connecting it to measure theory and modern statistics.

The central question that drove all of this work is deceptively simple: If I know every possible outcome, how do I assign a number to the chance that a particular outcome occurs? That question is exactly what this lesson answers. By the end, you will be able to list a sample space, identify simple events, and compute their probabilities with confidence.

SECTION 2

Core Principles & Definitions

Before calculating any probability, you need a clear vocabulary. The ideas below form the building blocks of every probability problem you will encounter in this course and beyond.

Experiment

An experiment is any process whose result is not known in advance — flipping a coin, rolling a die, or drawing a card from a shuffled deck.

Outcome

An outcome is a single possible result of an experiment. For a coin flip, the outcomes are Heads and Tails.

Sample Space (S)

The sample space is the complete set of all possible outcomes. For a standard die, S = {1, 2, 3, 4, 5, 6}.

Event

An event is any subset of the sample space. A simple event contains exactly one outcome, like rolling a 4.

Probability

The probability of an event is a number between 0 and 1 (inclusive) that measures how likely the event is to occur. A probability of 0 means impossible; 1 means certain.

✦ KEY TAKEAWAY

Think of the sample space like a playlist with every song loaded. An event is a particular song (or group of songs) you might hear. Probability tells you what fraction of the playlist matches the song you want. If your playlist has 20 songs and 1 is your favorite, the probability of hearing it on shuffle is 1 out of 20.

SECTION 3

Visual Explanation — Mapping a Sample Space

The diagram below shows the sample space for rolling a single standard six-sided die. Each face is an equally likely outcome. The highlighted region represents the event "rolling an even number," which includes the outcomes {2, 4, 6}. Notice how the probability is simply the count of highlighted outcomes divided by the total count.

The six squares represent each equally likely outcome when rolling a standard die. The three highlighted (cyan) squares show the event "rolling an even number." Dividing 3 favorable outcomes by 6 total outcomes yields a probability of 0.5.

The diagram makes an important principle visible: when every outcome in the sample space is equally likely, probability is just a ratio. Count the outcomes that satisfy your event, count the total outcomes, and divide. This ratio always falls between 0 and 1, where 0 means the event is impossible and 1 means it is guaranteed.

SECTION 4

Mathematical Framework

The classical probability formula is the cornerstone of this lesson. It applies whenever the outcomes in the sample space are equally likely — meaning no single outcome is more or less probable than any other.

CLASSICAL PROBABILITY FORMULA

P(A) = number of outcomes in A ÷ number of outcomes in S

P(A) = probability of event A; A = the event of interest (a subset of S); S = the sample space (the set of all possible outcomes).

COMPLEMENT RULE

P(not A) = 1 − P(A)

The probability that event A does not occur equals 1 minus the probability that it does. If P(rain) = 0.3, then P(no rain) = 0.7.

EXPERIMENTAL (RELATIVE FREQUENCY) PROBABILITY

P(A) ≈ number of times A occurred ÷ total number of trials

When you run a simulation or repeated experiment, you estimate probability by counting how often the event happened out of the total trials. As the number of trials grows, this value approaches the theoretical probability — a fact known as the Law of Large Numbers.

These three formulas cover the majority of problems you will face. The classical formula works when you can list outcomes and know they are equally likely. The complement rule offers a shortcut: sometimes it is easier to find the probability of what you don't want and subtract from 1. The experimental formula handles real-world data or computer simulations where theoretical calculations may not be practical.

SECTION 5

Theoretical vs. Experimental Probability

There are two main approaches to finding the probability of a simple event. Theoretical probability uses reasoning about the structure of the sample space — you count outcomes without performing the experiment. Experimental probability (also called empirical probability) is based on data collected from actual trials or simulations. Understanding when and why to use each approach is essential.

The violet curve represents the experimental proportion of heads over an increasing number of coin flips. Early results can be wildly off, but as the number of trials grows, the experimental value converges toward the theoretical probability of 0.50 (gold dashed line). This convergence is the Law of Large Numbers in action.

Comparison of theoretical and experimental approaches to probability

Feature	Theoretical Probability	Experimental Probability
Based on	Logical analysis of the sample space	Observed data from trials or simulations
Requires	All outcomes to be equally likely and countable	Running the experiment many times
Example	P(heads) = 1/2 for a fair coin	Flipped a coin 200 times; got 106 heads → 106/200 = 0.53
Best used when	You can list every outcome (dice, cards, spinners)	Outcomes aren't equally likely, or the situation is too complex to model

SECTION 6

Worked Example — Drawing from a Bag of Marbles

A bag contains 5 red marbles, 3 blue marbles, and 2 green marbles. You draw one marble at random without looking. What is the probability of drawing a blue marble? What is the probability of not drawing a blue marble?

Finding P(blue) and P(not blue)

Step 1 — Identify the Sample SpaceThe bag has 5 red + 3 blue + 2 green = 10 marbles total. Each marble is equally likely to be drawn, so the sample space has 10 equally likely outcomes.

|S| = 10

Step 2 — Count Favorable Outcomes for BlueThe event "draw a blue marble" contains exactly 3 outcomes (the 3 blue marbles). So the number of favorable outcomes is 3.

|A| = 3

Step 3 — Apply the Classical FormulaP(blue) = favorable outcomes ÷ total outcomes = 3 ÷ 10.

P(blue) = 3/10 = 0.3 = 30%

Step 4 — Use the Complement RuleP(not blue) = 1 − P(blue) = 1 − 0.3.

P(not blue) = 0.7 = 70%

Step 5 — VerifyThe non-blue marbles are 5 red + 2 green = 7. So P(not blue) = 7/10 = 0.7. This matches our complement calculation, confirming the answer.

✓ Verified: 0.3 + 0.7 = 1.0

SECTION 7

Strengths, Limitations & Common Errors

The classical probability formula is elegant and powerful, but it does have boundaries. Recognizing when the formula applies — and when it doesn't — will save you from common mistakes.

Strengths and limitations of the classical probability formula

Strengths	Limitations
Simple to compute: just count and divide.	Only works when outcomes are equally likely. A weighted die breaks the formula.
No experiment needed — pure reasoning gives an exact answer.	Requires that you can list every outcome, which is hard for very large or infinite sample spaces.
Works beautifully for dice, coins, cards, spinners, and lotteries.	Real-world events (e.g., weather, sports) rarely have equally likely outcomes.
The complement rule provides a handy shortcut for 'at least one' problems.	Students sometimes confuse the number of event types with the number of outcomes (e.g., saying P(even) = 1/2 'because there are 2 options: even or odd').

⚠️ Common Mistake Alert

A frequent error is assuming outcomes are equally likely when they aren't. For example, if you toss two coins and ask for P(one head), listing outcomes as {0 heads, 1 head, 2 heads} gives 1/3 — but that's wrong. The correct sample space is {HH, HT, TH, TT}, and P(one head) = 2/4 = 1/2. Always list individual outcomes, not categories.

✦ KEY TAKEAWAY

Think of the classical formula like a recipe: it only works if you start with the right ingredients. The 'right ingredients' here means a sample space where every single outcome has the same chance of happening. If the sample space is lopsided, switch to experimental probability or adjust your model.

SECTION 8

Connection to Compound Events & Advanced Probability

Simple event probability is the starting point for a much richer set of tools. Once you are comfortable computing P(A) for a single event, you will move on to compound events — situations involving two or more events combined with "and" or "or." The table below previews how this lesson's ideas connect to topics you'll see next.

How simple event probability connects to upcoming topics

This Lesson (Simple Events)	Coming Next (Compound Events)
One event A from one experiment	Two events A and B — are they independent?
P(A) = favorable ÷ total	P(A and B) = P(A) × P(B) if independent
Complement: P(not A) = 1 − P(A)	Addition Rule: P(A or B) = P(A) + P(B) − P(A and B)
Sample space listed as a set	Tree diagrams and two-way tables to organize larger sample spaces

Every advanced probability concept ultimately builds on what you learned today: defining a sample space, identifying favorable outcomes, and computing a ratio. Master this foundation, and the transition to compound events, conditional probability, and eventually statistical inference will feel natural.

SECTION 9

Practice Problems

PROBLEM 1 — CONCEPTUAL

A standard deck of 52 playing cards is shuffled. You draw one card. Is each card equally likely to be drawn? Explain why the classical probability formula applies (or does not apply) in this situation.

PROBLEM 2 — BASIC CALCULATION

A spinner is divided into 8 equal sections numbered 1 through 8. What is the probability of spinning a number greater than 5?

PROBLEM 3 — INTERMEDIATE

A bag holds 4 red, 6 white, and 2 blue marbles. You draw one marble at random. Find the probability of drawing a marble that is NOT red. Then verify your answer using both direct counting and the complement rule.

PROBLEM 4 — APPLIED

A quality-control inspector tested 500 smartphones from a production line and found that 15 had defective screens. Estimate the probability that a randomly selected phone from this line has a defective screen. Is this a theoretical or experimental probability? Explain.

PROBLEM 5 — CRITICAL THINKING

Two friends disagree about the probability that a thumbtack lands point-up when dropped. Alex says P(point-up) = 1/2 because there are two outcomes: point-up or point-down. Jordan says you cannot determine the probability without data. Who is correct, and why? Describe how you could settle the debate.

SUMMARY

Lesson Summary

In this lesson you learned to compute probabilities of simple events by identifying the sample space — the complete set of equally likely outcomes — and then applying the classical probability formula: P(A) = favorable outcomes ÷ total outcomes. You also explored the complement rule — P(not A) = 1 − P(A) — which provides a powerful shortcut when it is easier to count what you don't want.

You distinguished between theoretical probability, which relies on logical analysis of a balanced sample space, and experimental probability, which uses data from repeated trials or simulations. The Law of Large Numbers guarantees that experimental probability converges toward the theoretical value as the number of trials grows. With these tools, you are prepared to tackle compound events and more advanced probability topics ahead.