Which statement best explains conditional probability and independence?
When two separate events, A and B, are independent,
P(A and B) P(A).P(B)
P(BA)
P(B). This means that the
P(A)
P(A)
occurrence of event B first did not affect the probability of event A occurring next.
When two separate events, A and B, are independent,
P(A and B) P(A).P(B)
P(BA)
– P(B) This means that the
P(A)
P(A)
occurrence of event B first affected the probability of event A occurring next.
When two separate events, A and B, are independent,
P(A and B) P(A.P(B)
P(BA)
P(B)
P(A)
This means that the
PA
occurrence of event A first did not affect the probability of event B occurring next.
When two separate events, A and B are independent,
P(A and B) P(A).P(B)
P(BA)
P(B)
P(A)
P(A)
This means that the
Answer:
The answer of the question would be: C)
Step-by-step explanation:
When two separate events, A and B, are independent, P(A|B)=P(B). This means that the probability of event B occurring first has no effect on the probability of event A occurring next. Because of the evident independence.