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🎯⭐ INTERACTIVE LESSON

Probability Basics

Learn step-by-step with interactive practice!

Probability Basics - Complete Interactive Lesson

Part 1: What Is Probability?

🎲 Probability Basics

Part 1 of 5 — What Is Probability?


Topics in This Part

Section
What Probability Measures
The 0-to-1 Scale
Likely, Unlikely, and Everything Between

🔑 Key Concept: Probability is a number that tells you how likely an event is to happen. It always lands between 00 (impossible) and 11 (certain).

What Probability Measures

Some things are sure to happen, some can never happen, and most things are somewhere in between. Probability puts a number on that "in-between."

A few words we'll use the whole lesson:

WordMeaningExample
ExperimentAn action with an uncertain resultRolling a die
OutcomeOne possible resultRolling a 44
EventA result (or group of results) we care aboutRolling an even number

💡 An event can be one outcome (rolling a 44) or several outcomes grouped together (rolling an even number: 22, 44, or 66).

The 0-to-1 Scale

Every probability is a number from 00 to 11. You can write it as a fraction, a decimal, or a percent.

0≤P(event)≤10 \le P(\text{event}) \le 1

ProbabilityMeansIn words
000%0\%Impossible — can never happen
12\frac{1}{2}50%50\%Even chance — just as likely as not
11100%100\%Certain — sure to happen

The closer the number is to 11, the more likely the event. The closer to 00, the less likely.

⚠️ A probability can never be negative and can never be more than 11. If you ever get an answer like 1.41.4 or −12-\frac{1}{2}, you made a mistake somewhere.

Concept Check 🎯

Likely, Unlikely, and Everything Between

We often describe events in words. Here is how the words line up with the scale:

Probability rangeDescription
Exactly 00Impossible
Between 00 and 12\frac{1}{2}Unlikely
Exactly 12\frac{1}{2}Equally likely (even chance)
Between 12\frac{1}{2} and 11Likely
Exactly 11Certain

Example: The probability of flipping heads on a fair coin is 12\frac{1}{2} — an even chance.

Example: The probability that the sun rises tomorrow is essentially 11 — certain.

Example: The probability of rolling a 77 on a standard 66-sided die is 00 — impossible (there is no 77).

Match the Description 🔽

Pick the best word for each probability.

Putting Numbers on the Scale

You don't always need a formula to place a probability — sometimes you can reason about how "fair" a situation is.

  • A fair coin has two equally likely sides, so P(heads)=12P(\text{heads}) = \frac{1}{2}.
  • A certain event always gets 11; an impossible one always gets 00.
  • If something is twice as likely to fail as to succeed, success gets the smaller share.

💡 Quick gut-check: before computing, ask "should my answer be near 00, near 12\frac{1}{2}, or near 11?" If your final number disagrees with your gut, re-check the count.

Place It on the Scale 🧮

Give each probability as a number from 00 to 11.

1) A spinner has 44 equal sections. P(landing on one chosen section)= ?P(\text{landing on one chosen section}) = \,? (fraction) 2) P(a certain event)= ?P(\text{a certain event}) = \,? 3) P(an impossible event)= ?P(\text{an impossible event}) = \,?

Part 2: Theoretical Probability & Sample Spaces

🎲 Probability Basics

Part 2 of 5 — Theoretical Probability & Sample Spaces


🔑 The Formula: When every outcome is equally likely, P(event)=number of favorable outcomestotal number of outcomesP(\text{event}) = \frac{\text{number of favorable outcomes}}{\text{total number of outcomes}}

The Sample Space

The sample space is the list of all possible outcomes of an experiment. Counting it correctly is the key to every probability problem.

ExperimentSample spaceTotal outcomes
Flip a coinH, T22
Roll a die1,2,3,4,5,61, 2, 3, 4, 5, 666
Spin a 44-color spinnerred, blue, green, yellow44

A favorable outcome is any outcome that counts as the event you want.

💡 "Favorable" does not mean "good" — it just means it matches the event you are measuring. If your event is "roll an odd number," the favorable outcomes are 11, 33, and 55.

Theoretical Probability

Theoretical probability is what should happen based on counting, when all outcomes are equally likely.

P(event)=favorable outcomestotal outcomesP(\text{event}) = \frac{\text{favorable outcomes}}{\text{total outcomes}}

Worked Example: Rolling an even number on a die

  • Total outcomes: 66 (the numbers 11 through 66)
  • Favorable outcomes: 33 (the evens 22, 44, 66)

P(even)=36=12P(\text{even}) = \frac{3}{6} = \frac{1}{2}

Worked Example: Drawing a red marble

A bag has 44 red, 33 blue, and 11 green marble. Total =4+3+1=8= 4 + 3 + 1 = 8.

P(red)=48=12P(\text{red}) = \frac{4}{8} = \frac{1}{2}

🔑 Always simplify your fraction when you can — 48\frac{4}{8} and 12\frac{1}{2} mean the same probability.

Concept Check 🎯

Counting Carefully

The whole skill is counting: how many outcomes match the event, and how many outcomes there are in total.

For an event like "greater than 44" on a die, list them out so you don't miss any: 55 and 66 — that's 22 favorable outcomes out of 66.

💡 When in doubt, write the sample space (1,2,3,4,5,61,2,3,4,5,6) and circle the favorable ones. Now try a few on your own.

Find the Probability 🧮

A standard 66-sided die is rolled. Write each probability as a simplified fraction (like 1/2).

1) P(rolling a 5)= ?P(\text{rolling a } 5) = \,? 2) P(rolling an odd number)= ?P(\text{rolling an odd number}) = \,? 3) P(rolling a number greater than 4)= ?P(\text{rolling a number greater than } 4) = \,?

Spinners Work the Same Way

A spinner with equal sections behaves just like a die: each section is one equally likely outcome.

If a spinner has 88 equal sections, the total number of outcomes is 88, and you count favorable sections the same way you counted die faces.

⚠️ This only works when the sections are the same size. Unequal sections are not equally likely, so the simple counting formula would not apply.

Spinner Probabilities 🔽

A spinner has 88 equal sections numbered 11 through 88. Choose the correct probability.

Part 3: Complements: The Probability of "Not"

🎲 Probability Basics

Part 3 of 5 — Complements: The Probability of "Not"


🔑 The Complement Rule: The probability that an event does not happen is P(not A)=1−P(A)P(\text{not } A) = 1 - P(A)

What Is a Complement?

The complement of an event AA is "everything except AA" — all the outcomes where AA does not happen.

Because something either happens or it doesn't, the two probabilities must add to 11:

P(A)+P(not A)=1P(A) + P(\text{not } A) = 1

Rearranging gives the rule:

P(not A)=1−P(A)P(\text{not } A) = 1 - P(A)

Worked Example: Not rolling a 6

On a die, P(rolling a 6)=16P(\text{rolling a } 6) = \frac{1}{6}. So:

P(not 6)=1−16=66−16=56P(\text{not } 6) = 1 - \frac{1}{6} = \frac{6}{6} - \frac{1}{6} = \frac{5}{6}

💡 Why this is handy: counting "all the ways something does NOT happen" can be slow. The complement lets you count the one thing it does, then subtract from 11.

Worked Example: Marbles

A bag has 1010 marbles: 77 are red and 33 are not red.

P(red)=710P(\text{red}) = \frac{7}{10}

P(not red)=1−710=1010−710=310P(\text{not red}) = 1 - \frac{7}{10} = \frac{10}{10} - \frac{7}{10} = \frac{3}{10}

Notice 710+310=1010=1\frac{7}{10} + \frac{3}{10} = \frac{10}{10} = 1. ✓

Quick check with percents

If there is a 30%30\% chance of rain, then the chance of no rain is:

100%−30%=70%100\% - 30\% = 70\%

⚠️ The complement of "at least one" is "none," and the complement of "all" is "not all." Read the event carefully before you subtract.

Concept Check 🎯

Two Ways to the Same Answer

For "not red" with 33 red out of 55 marbles, you can either:

  1. Count directly — there are 22 non-red marbles, so 25\frac{2}{5}, or
  2. Use the complement — 1−35=251 - \frac{3}{5} = \frac{2}{5}.

Both give the same answer. The complement is the faster route when there are many ways to "not" happen. Try it below.

Use the Complement 🧮

Answer with a simplified fraction (like 5/6) unless told otherwise.

1) On a die, P(not rolling a 1)= ?P(\text{not rolling a } 1) = \,? 2) A bag is 38\frac{3}{8} green marbles. P(not green)= ?P(\text{not green}) = \,? 3) If P(event)=0.4P(\text{event}) = 0.4 as a decimal, then P(not event)= ?P(\text{not event}) = \,? (decimal)

Quick Recap

The complement rule shows up everywhere — weather, games, marbles, spinners. The setup is always the same:

P(not A)=1−P(A)P(\text{not } A) = 1 - P(A)

And the two probabilities always add back to 11. Use both ideas in the dropdown below.

Complements 🔽

A spinner has 55 equal sections: 33 red and 22 blue. Fill in each probability.

Part 4: Experimental Probability & Predictions

🎲 Probability Basics

Part 4 of 5 — Experimental Probability & Predictions


🔑 Theory vs. Experiment: Theoretical probability comes from counting. Experimental probability comes from actually doing the experiment and recording results.

Experimental Probability

Experimental probability (also called relative frequency) is based on data you collect:

P(event)=number of times the event happenedtotal number of trialsP(\text{event}) = \frac{\text{number of times the event happened}}{\text{total number of trials}}

Worked Example: Flipping a coin

Suppose you flip a coin 2020 times and get heads 1212 times.

P(heads)=1220=35=0.6P(\text{heads}) = \frac{12}{20} = \frac{3}{5} = 0.6

The theoretical probability of heads is 12=0.5\frac{1}{2} = 0.5. The experiment gave 0.60.6 — close, but not exact.

💡 The Law of Large Numbers (in plain words): the more trials you run, the closer your experimental probability usually gets to the theoretical probability.

Worked Example: Reading a results table

A student spun a 44-color spinner 5050 times:

ColorTimes spun
Red1515
Blue1010
Green2020
Yellow55

The total is 15+10+20+5=5015 + 10 + 20 + 5 = 50. The experimental probability of green is:

P(green)=2050=25=0.4P(\text{green}) = \frac{20}{50} = \frac{2}{5} = 0.4

And the experimental probability of yellow is:

P(yellow)=550=110=0.1P(\text{yellow}) = \frac{5}{50} = \frac{1}{10} = 0.1

⚠️ For experimental probability, the denominator is the total number of trials (5050), not the number of colors (44).

Concept Check 🎯

Comparing the Two

Use the spinner table from above again:

ColorTimes spunExperimental probability
Red15151550=310\frac{15}{50} = \frac{3}{10}
Blue10101050=15\frac{10}{50} = \frac{1}{5}
Green20202050=25\frac{20}{50} = \frac{2}{5}
Yellow55550=110\frac{5}{50} = \frac{1}{10}

If the spinner were perfectly fair, each color's theoretical probability would be 14\frac{1}{4} (one color out of four equal sections). Notice the experimental values are near 14\frac{1}{4} but not equal — that's normal for only 5050 spins.

💡 The four experimental probabilities should still add to 11: 1550+1050+2050+550=5050=1\frac{15}{50}+\frac{10}{50}+\frac{20}{50}+\frac{5}{50} = \frac{50}{50} = 1. ✓

Theoretical vs. Experimental 🔽

A coin is flipped 4040 times and lands heads 2424 times. Fill in each blank.

Making Predictions

You can use probability to predict how many times something will happen in many trials:

predicted count=P(event)×(number of trials)\text{predicted count} = P(\text{event}) \times (\text{number of trials})

Worked Example

If P(heads)=12P(\text{heads}) = \frac{1}{2} and you flip a coin 8080 times, you'd predict:

12×80=40 heads\frac{1}{2} \times 80 = 40 \text{ heads}

Worked Example

If P(red)=14P(\text{red}) = \frac{1}{4} on a spinner and you spin 6060 times, you'd predict:

14×60=15 reds\frac{1}{4} \times 60 = 15 \text{ reds}

💡 A prediction is an estimate. Real results will usually be close but not exactly equal.

Predict the Count 🧮

Use predicted count=P×(trials)\text{predicted count} = P \times (\text{trials}). Enter a whole number.

1) P(heads)=12P(\text{heads}) = \frac{1}{2}, flip 5050 times. Predict heads:  ?\,? 2) P(rolling a 6)=16P(\text{rolling a } 6) = \frac{1}{6}, roll 6060 times. Predict sixes:  ?\,? 3) P(blue)=14P(\text{blue}) = \frac{1}{4}, spin 100100 times. Predict blues:  ?\,?

Part 5: Mixed Practice & Mastery Check

🎲 Probability Basics

Part 5 of 5 — Mixed Practice & Mastery Check


You can now (1) place a probability on the 00-to-11 scale, (2) compute theoretical probability by counting, (3) use the complement rule, and (4) work with experimental probability and predictions. Let's put it all together.

Quick Reference

GoalKey move
Theoretical probabilityfavorabletotal\dfrac{\text{favorable}}{\text{total}}
Probability of "not AA"1−P(A)1 - P(A)
Experimental probabilitytimes it happenedtotal trials\dfrac{\text{times it happened}}{\text{total trials}}
Predict a countP(event)×trialsP(\text{event}) \times \text{trials}
Valid range0≤P≤10 \le P \le 1

⚠️ Remember: always simplify fractions, never let a probability exceed 11 or go below 00, and an event plus its complement always add to 11.

Mixed Practice 🎯

One Spinner, Every Skill

The next drill uses a single 1010-section spinner to touch all four skills at once: counting favorable outcomes, simplifying, taking a complement, and predicting a count.

💡 Take them one at a time. For each, decide first: am I counting a probability, subtracting for a complement, or multiplying to predict?

Final Drill 🧮

A spinner has 1010 equal sections numbered 11 to 1010.

1) P(landing on a number>7)P(\text{landing on a number} > 7) as a simplified fraction = ?= \,? 2) P(NOT landing on 1)P(\text{NOT landing on } 1) as a simplified fraction = ?= \,? 3) If you spin 4040 times, predict the number of times you land on an even number. (whole number) = ?= \,?

You're Ready

That's the whole toolkit: the 00-to-11 scale, theoretical probability from counting, the complement rule, and experimental probability with predictions.

The Exit Quiz below pulls one question from each big idea. Read each carefully and decide which tool fits before you answer.

Exit Quiz ✅

Answer all three to finish the lesson.