- #1
CAH said:I know the equations that P(A|B') = P(AnB) / P(B')
But why isn't it P(A) - P(A n B)
The formula for conditional probability P(A|B') takes into account the fact that event B did not occur. This means that the sample space is reduced to only those outcomes where B did not happen, which affects the probability of event A occurring.
The formula P(A|B') takes the probability of event A occurring in the reduced sample space of B' (B not occurring) and normalizes it by dividing it by the probability of B' occurring. This accounts for the fact that event B did not occur and adjusts the probability of event A accordingly.
Assume you have a bag with 10 red and 10 blue marbles. If you draw a marble without replacement, the probability of drawing a red marble (event A) is 10/20 = 1/2. However, if you know that the marble drawn was not blue (event B'), the sample space is reduced to 10 red marbles, making the probability of drawing a red marble now 10/10 = 1. This is different from the formula P(A)-P(A n B), which would give a probability of 1/2 – 0 = 1/2.
The formula P(A|B') should be used when you know that event B did not occur and you want to adjust the probability of event A accordingly. For example, in medical diagnosis, if a patient tests negative for a certain disease (event B'), the probability of them having the disease (event A) will be different from the overall probability of having the disease.
Yes, P(A|B') is used in many different fields such as genetics, finance, and engineering. It is commonly used in Bayesian statistics, where prior knowledge or information is used to update probabilities when new evidence is presented. It is also used in decision theory to calculate the expected utility of an action given that certain events have not occurred.