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Statistics and Probability - Conditional probability

Grade 11IB_AI

Review the key concepts, formulae, and examples before starting your quiz.

🔑Concepts

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Conditional probability is the probability of an event AA occurring given that another event BB has already occurred. It is denoted as P(A∣B)P(A|B).

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The 'given' event BB effectively restricts the sample space to only those outcomes contained in BB.

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Two events AA and BB are considered independent if the occurrence of one does not change the probability of the other. This can be tested using P(A∣B)=P(A)P(A|B) = P(A) or P(B∣A)=P(B)P(B|A) = P(B).

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Tree diagrams are frequently used in IB AI to represent conditional probabilities. The first set of branches shows P(B)P(B) and P(B′)P(B'), while the second set shows P(A∣B)P(A|B) and P(A∣B′)P(A|B').

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A contingency table (two-way table) can be used to calculate conditional probabilities by looking at the specific row or column corresponding to the 'given' condition.

📐Formulae

P(A∣B)=P(A∩B)P(B)P(A|B) = \frac{P(A \cap B)}{P(B)}

P(A∩B)=P(B)×P(A∣B)P(A \cap B) = P(B) \times P(A|B) (The Multiplication Rule)

P(A∩B)=P(A)×P(B)P(A \cap B) = P(A) \times P(B) (Condition for independence)

P(A∣B)=P(A)P(A|B) = P(A) (Alternative condition for independence)

💡Examples

Problem 1:

In a class of 30 students, 18 study Biology (BB), 15 study Chemistry (CC), and 8 study both. A student is selected at random. Find the probability that the student studies Biology, given that they study Chemistry.

Solution:

We are looking for P(B∣C)P(B|C). From the information given: n(C)=15n(C) = 15 n(B∩C)=8n(B \cap C) = 8 Using the formula for conditional probability based on frequencies: P(B∣C)=n(B∩C)n(C)P(B|C) = \frac{n(B \cap C)}{n(C)} P(B∣C)=815P(B|C) = \frac{8}{15} P(B∣C)≈0.533P(B|C) \approx 0.533

Explanation:

To find P(B∣C)P(B|C), we only consider the group of students who study Chemistry (the denominator). Within that specific group, we count how many also study Biology (the numerator).

Problem 2:

Given that P(A)=0.6P(A) = 0.6, P(B)=0.5P(B) = 0.5, and P(A∪B)=0.8P(A \cup B) = 0.8, determine if events AA and BB are independent.

Solution:

First, find P(A∩B)P(A \cap B) using the addition rule: P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B) 0.8=0.6+0.5−P(A∩B)0.8 = 0.6 + 0.5 - P(A \cap B) P(A∩B)=1.1−0.8=0.3P(A \cap B) = 1.1 - 0.8 = 0.3

Now, check the condition for independence P(A∩B)=P(A)×P(B)P(A \cap B) = P(A) \times P(B): P(A)×P(B)=0.6×0.5=0.3P(A) \times P(B) = 0.6 \times 0.5 = 0.3

Since P(A∩B)=0.3P(A \cap B) = 0.3 and P(A)×P(B)=0.3P(A) \times P(B) = 0.3, the values are equal.

Explanation:

Because the probability of the intersection is equal to the product of the individual probabilities, events AA and BB are independent.

Problem 3:

A bag contains 5 red balls and 3 blue balls. Two balls are drawn one after the other without replacement. Find the probability that the second ball is blue, given that the first ball was red.

Solution:

Let R1R_1 be the event that the first ball is red and B2B_2 be the event that the second ball is blue. Initially, there are 5+3=85 + 3 = 8 balls. If the first ball is red, it is not replaced. Now the bag contains: 5−1=45 - 1 = 4 red balls 33 blue balls Total balls remaining =7= 7 P(B2∣R1)=37P(B_2|R_1) = \frac{3}{7}

Explanation:

Without replacement means the total count and the count of the specific color change for the second draw. The probability is calculated based on the state of the bag after the first event has occurred.