Probability concepts, conditional probability, independent events

IB Mathematics Applications and Interpretation HL· IB AI HL 5.4· 45 min read

1. Core Probability Fundamentals★★☆☆☆⏱ 15 min

📘 Definition

Sample Space and Event

$S$ for sample space, $A, B$ for events

The sample space is the set of all possible outcomes of a random experiment. An event is a subset of the sample space.

Example:

Rolling a 6-sided die: $S = {1, 2, 3, 4, 5, 6}$ , event "roll even" is $A = {2, 4, 6}$

All probability follows core basic rules: the probability of any event is between 0 and 1, and the sum of probabilities of all outcomes in the sample space equals 1. A very useful rule is the complement rule for the event that $A$ does not occur, written $A^{'}$ .

P(A') = 1 - P(A)

📐 Worked Example

A fair 10-sided die is rolled once. Find the probability that the outcome is not a multiple of 3.

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1. Identify the sample space and event:
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$S = \{1,2,...,10\}, \quad n(S) = 10$
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Let $A$ = outcome is a multiple of 3, so $A = {3, 6, 9}$ , $n (A) = 3$
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1. Calculate $P (A)$ :
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$P(A) = \frac{n(A)}{n(S)} = \frac{3}{10}$
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1. Use the complement rule to find $P (A^{'})$ , the probability the outcome is not a multiple of 3:
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$P(A') = 1 - P(A) = 1 - \frac{3}{10} = \frac{7}{10}$

2. Conditional Probability★★★☆☆⏱ 20 min

📘 Definition

Conditional Probability

The probability of event $A$ occurring given that event $B$ has already occurred is called conditional probability, written $P (A ∣ B)$ . The formula is:

Example:

Probability of drawing a red card second, given the first card drawn was red

P(A|B) = \frac{P(A \cap B)}{P(B)}, \quad P(B) > 0

Conditional probability restricts the sample space to only outcomes where $B$ has occurred, so we normalize the joint probability $P (A \cap B)$ by the total probability of $B$ . This matches our intuitive understanding of how prior information changes probability.

📐 Worked Example

A bag contains 5 red marbles and 7 blue marbles. Two marbles are drawn without replacement. Find the probability that the second marble is red, given the first was red.

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1. Define events: $A$ = second marble red, $B$ = first marble red. We need $P (A ∣ B)$ .
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1. Calculate the joint probability both are red:
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$P(A \cap B) = \frac{5}{12} \times \frac{4}{11} = \frac{20}{132} = \frac{5}{33}$
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1. We know $P (B) = \frac{5}{12}$ , the probability the first marble is red.
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1. Apply the conditional probability formula:
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$P(A|B) = \frac{P(A \cap B)}{P(B)} = \frac{\frac{5}{33}}{\frac{5}{12}} = \frac{12}{33} = \frac{4}{11}$
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This matches intuition: after drawing one red, 4 red out of 11 total marbles remain.

Exam tip:

Always confirm $P (B) \neq = 0$ ; if $B$ cannot occur, conditional probability is undefined, a common trick exam question.

3. Independent Events and Key Distinctions★★★☆☆⏱ 20 min

📘 Definition

Independent Events

Two events $A$ and $B$ are independent if the occurrence of one does not change the probability of the other. This gives the core mathematical condition:

Example:

Rolling a die twice: the result of the first roll does not affect the second

P(A \cap B) = P(A) \times P(B)

Mutually exclusive events are events that cannot occur at the same time, so $P (A \cap B) = 0$ . This is a completely separate concept from independence, and mixing the two up is one of the most common exam mistakes.

📐 Worked Example

A student takes two independent tests. The probability they pass test 1 is 0.7, and the probability they pass test 2 is 0.6. Find the probability the student passes exactly one test.

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1. Define events: $P_{1}$ = pass test 1, $P_{2}$ = pass test 2, events are independent.
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1. "Pass exactly one" means (pass 1 and fail 2) OR (fail 1 and pass 2). These are mutually exclusive, so we add their probabilities.
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1. Calculate the first probability using the independence rule:
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$P(P_1 \cap P_2') = P(P_1) \times P(P_2') = 0.7 \times (1 - 0.6) = 0.28$
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1. Calculate the second probability:
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$P(P_1' \cap P_2) = P(P_1') \times P(P_2) = (1 - 0.7) \times 0.6 = 0.18$
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1. Add the probabilities:
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$P(\text{exactly one pass}) = 0.28 + 0.18 = 0.46$

4. Common Pitfalls

Wrong move:

Confusing independent and mutually exclusive events for non-zero probability events

Why:

These are separate concepts, and their conditions are contradictory for events with positive probability

Correct move:

Remember: mutually exclusive means $P (A \cap B) = 0$ , independent means $P (A \cap B) = P (A) P (B)$ . Only use mutually exclusive if events cannot occur together.

Wrong move:

Forgetting to divide by $P (B)$ when calculating conditional probability, leaving $P (A \cap B)$ as the final answer

Why:

Conditional probability restricts the sample space to $B$ , so normalization is required

Correct move:

Always write the full formula $P (A ∣ B) = \frac{P ( A \cap B )}{P ( B )}$ and divide by $P (B)$ before finishing.

Wrong move:

Treating draws with replacement the same as draws without replacement (or vice versa)

Why:

The sample size only changes when drawing without replacement, which changes probabilities for subsequent draws

Correct move:

Always read the question carefully to check if draws are with or without replacement before starting your calculation.

Wrong move:

Assuming all events are independent when the question does not state this

Why:

Many exam problems test your ability to check for independence, not assume it from the start

Correct move:

Only use the rule $P (A \cap B) = P (A) P (B)$ if the question explicitly states events are independent, or you have verified independence via the definition.

5. Quick Reference Cheatsheet

Concept	Formula / Rule
Complement Rule	$P (A^{'}) = 1 - P (A)$
Conditional Probability	$P (A ∣ B) = \frac{P ( A \cap B )}{P ( B )}, P (B) > 0$
General Multiplication Rule	$P (A \cap B) = P (A ∣ B) P (B)$
Independent Events	$P (A \cap B) = P (A) P (B)$ , $P (A ∣ B) = P (A)$
Mutually Exclusive Events	$P (A \cup B) = P (A) + P (B)$ , $P (A \cap B) = 0$

When this came up on past exams

AI-estimated based on syllabus patterns — cross-check with official past papers for accuracy. Use only as revision-focus signals.

2021 · 1
Conditional probability calculation
2022 · 2
Independent events problem
2023 · 1
Venn diagram probability

Going deeper

syllabusIB AI HL Official SyllabusOfficial subtopic reference

What's Next

Mastering these core probability concepts is the foundation for all further probability and statistics topics in IB AI HL, including discrete and continuous probability distributions, expected value, hypothesis testing, and regression analysis. Conditional probability is especially critical for Bayes' theorem, a common higher-mark exam topic that extends the ideas you learned here. The distinction between independent and dependent events also comes into play when checking for association in contingency tables for chi-squared tests, so building a solid understanding here will help you avoid common mistakes in more advanced topics.