Final answer:
The statement is false; P(A | B) means the probability of A given B. It is a measure of how the occurrence of B affects the probability of A, calculated using a specific formula where P(B) must not be zero.
Step-by-step explanation:
The statement "P(A | B) can be read as 'the probability that B occurs given that A has occurred'" is false. Instead, P(A | B) should be read as 'the probability that A occurs given that B has already occurred'. This is called conditional probability, and it is used when the occurrence of one event affects the probability of another. To calculate P(A | B), the formula is:
P(A | B) = P(A AND B) / P(B)
where P(A AND B) is the probability of both events A and B occurring, and P(B) is the probability of event B occurring. It's important to note that for this formula to be valid, P(B) must be greater than zero. If events A and B are independent, then the calculation simplifies to P(A AND B) = P(A)P(B), since the occurrence of B does not affect the probability of A.