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Statistics and Probability - Conditional probability – Independent events

Grade 11IB_AA

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

🔑Concepts

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Conditional Probability: The probability of an event AA occurring given that event BB has already occurred is denoted by P(A∣B)P(A|B). This reduces the sample space to the outcomes within BB.

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Independent Events: Two events AA and BB are independent if the occurrence of one does not affect the probability of the other. Mathematically, P(A∣B)=P(A)P(A|B) = P(A) and P(B∣A)=P(B)P(B|A) = P(B).

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Product Rule for Independence: For two independent events, the probability of both occurring is the product of their individual probabilities: P(A∩B)=P(A)×P(B)P(A \cap B) = P(A) \times P(B).

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The General Multiplication Rule: For any two events, P(A∩B)=P(B)×P(A∣B)P(A \cap B) = P(B) \times P(A|B) or P(A∩B)=P(A)×P(B∣A)P(A \cap B) = P(A) \times P(B|A).

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Testing for Independence: To determine if two events are independent, verify if P(A∩B)=P(A)P(B)P(A \cap B) = P(A)P(B). If this equality does not hold, the events are dependent (contingent).

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Tree Diagrams: Used to represent sequential events. The probability of a path is the product of the probabilities along the branches. Conditional probabilities are represented on the second set of branches.

📐Formulae

P(A∣B)=P(A∩B)P(B), where P(B)>0P(A|B) = \frac{P(A \cap B)}{P(B)}, \text{ where } P(B) > 0

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

P(A∩B)=P(A)×P(B) (if and only if A and B are independent)P(A \cap B) = P(A) \times P(B) \text{ (if and only if } A \text{ and } B \text{ are independent)}

P(A′∣B)=1−P(A∣B)P(A'|B) = 1 - P(A|B)

💡Examples

Problem 1:

In a group of 100 students, 60 study Mathematics (MM) and 40 study Physics (PP). If 24 students study both subjects, find the probability that a student studies Physics given that they study Mathematics. Determine if the events MM and PP are independent.

Solution:

First, we find the individual probabilities: P(M)=60100=0.6P(M) = \frac{60}{100} = 0.6 P(P)=40100=0.4P(P) = \frac{40}{100} = 0.4 P(M∩P)=24100=0.24P(M \cap P) = \frac{24}{100} = 0.24

To find P(P∣M)P(P|M): P(P∣M)=P(P∩M)P(M)=0.240.6=0.4P(P|M) = \frac{P(P \cap M)}{P(M)} = \frac{0.24}{0.6} = 0.4

To check for independence, we check if P(M∩P)=P(M)×P(P)P(M \cap P) = P(M) \times P(P): P(M)×P(P)=0.6×0.4=0.24P(M) \times P(P) = 0.6 \times 0.4 = 0.24 Since P(M∩P)=0.24P(M \cap P) = 0.24 and P(M)×P(P)=0.24P(M) \times P(P) = 0.24, the events are independent.

Explanation:

We use the definition of conditional probability to find P(P∣M)P(P|M). Since P(P∣M)=P(P)P(P|M) = P(P), it confirms independence. The calculation of the intersection matching the product of individual probabilities also proves independence.

Problem 2:

Events AA and BB are such that P(A)=0.7P(A) = 0.7 and P(B)=0.2P(B) = 0.2. If AA and BB are independent, find P(A∪B)P(A \cup B).

Solution:

Since AA and BB are independent: P(A∩B)=P(A)×P(B)=0.7×0.2=0.14P(A \cap B) = P(A) \times P(B) = 0.7 \times 0.2 = 0.14

Now use 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) Substituting the values: P(A∪B)=0.7+0.2−0.14P(A \cup B) = 0.7 + 0.2 - 0.14 Summing the first two terms: 0.70+0.200.90\begin{array}{r} 0.70 \\ + 0.20 \\ \hline 0.90 \end{array} Subtracting the intersection: 0.90−0.140.76\begin{array}{r} 0.90 \\ - 0.14 \\ \hline 0.76 \end{array} So, P(A∪B)=0.76P(A \cup B) = 0.76.

Explanation:

Independence allows us to calculate the intersection P(A∩B)P(A \cap B) by multiplying the given probabilities. We then apply the General Addition Rule to find the union.

Problem 3:

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

Solution:

Let R1R_1 be the event that the first marble is red, and B2B_2 be the event that the second marble is blue. Initially, there are 5+3=85 + 3 = 8 marbles. If the first marble drawn is red, there are now 5−1=45 - 1 = 4 red marbles and 3 blue marbles remaining in the bag. Total marbles remaining = 4+3=74 + 3 = 7.

The probability of drawing a blue marble now is: P(B2∣R1)=37P(B_2 | R_1) = \frac{3}{7}

Explanation:

This is a classic conditional probability problem where the sample space changes. Since the first marble was red and not replaced, the total count decreases by 1, but the count of blue marbles remains the same.