Step 1: Understanding the Concept:
This question deals with conditional probability for independent events. Two events are independent if the occurrence of one does not affect the probability of the other. The notation \( P(B|A) \) represents the conditional probability of event B occurring given that event A has already occurred.
Step 2: Key Formula or Approach:
The definition of independent events A and B is that \( P(A \cap B) = P(A) \cdot P(B) \).
The formula for conditional probability is \( P(B|A) = \frac{P(A \cap B)}{P(A)} \).
For independent events, this simplifies directly to \( P(B|A) = P(B) \).
Step 3: Detailed Explanation:
Given that events A and B are independent.
By the definition of independence, the probability of B occurring is not affected by whether A has occurred or not.
Therefore, the probability of B given A is simply the probability of B.
\[ P(B|A) = P(B) \]
We are given that \( P(B) = 0.4 \).
So,
\[ P(B|A) = 0.4 \]
Alternative Method (using formula):
First, calculate \( P(A \cap B) \) using the independence rule:
\[ P(A \cap B) = P(A) \cdot P(B) = (0.3)(0.4) = 0.12 \]
Now, use the conditional probability formula:
\[ P(B|A) = \frac{P(A \cap B)}{P(A)} = \frac{0.12}{0.3} = \frac{12}{30} = \frac{4}{10} = 0.4 \]
Both methods yield the same result.
Step 4: Final Answer:
The value of \( P(B|A) \) is 0.4.