Proof that P(A) = 1, P(B) = 1, then P(AB) = 1

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In summary, the theorem states that if the probabilities of events A and B both equal 1, then the probability of both A and B occurring is also 1. This can be proved by using the fact that P(A U B) >= P(A) and knowing that P(A) = 1, which leads to the conclusion that P(A U B) = 1. The formula P(A U B) = P(A) + P(B) - P(AB) is also used to show that P(AB) = 1.
  • #1
hholzer
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Theorem:
If P(A) = 1, P(B) = 1, then P(AB) = 1

My book starts out with the proof as follows:

P(A U B) >= P(A) = 1, so P(A U B) = 1

How do they reach such a conclusion?

Things I know:
P(A U B) = P(A) + P(B) - P(AB)

How can I use that to be sure that P(A U B) = 1?
 
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  • #2
How does the fact that [tex] A \subset A \cup B [/tex]

verify that [tex] P(A \cup B) \ge P(A) [/tex]?

Second: IF you understand why the first comment is true, how does it, coupled with [tex] P(A) = 1 [/tex] show [tex] P(A \cup B) = 1 [/tex]?
 
  • #3
hholzer said:
Theorem:
If P(A) = 1, P(B) = 1, then P(AB) = 1

My book starts out with the proof as follows:

P(A U B) >= P(A) = 1, so P(A U B) = 1

It is true that P(A U B) >= P(A). We know that P(A) = 1. Read P(A U B) as the probability of A OR B occurring (maybe both). Well, the probablility of A is 1, so A U B will always be true regardless of if B happens or not. So since P(A U B) >= P(A), P(A) = 1 and a probablility greater than 1 is not possible, the only value we can have for P(A U B) is 1. So:

P(A U B) = P(A) + P(B) - P(AB) -> 1 = 1 + 1 - P(AB) -> P(AB) = 1
 

1. What is the meaning of P(A) = 1, P(B) = 1, and P(AB) = 1 in probability theory?

In probability theory, P(A) = 1 means that the probability of event A occurring is 100%, or certain. Similarly, P(B) = 1 means that the probability of event B occurring is also 100%. P(AB) = 1 means that the joint probability of events A and B occurring together is also 100%, indicating a strong relationship between the two events.

2. Why is it important to prove that P(A) = 1, P(B) = 1, then P(AB) = 1?

Proving that P(A) = 1, P(B) = 1, then P(AB) = 1 is important because it shows that events A and B are highly correlated and have a strong relationship. This information can be useful in decision making and predicting outcomes in various fields, such as statistics, economics, and science.

3. What is the difference between independent and dependent events in probability theory?

In probability theory, independent events are events that do not affect each other's probability and occur separately. On the other hand, dependent events are events that are affected by each other's probability and occur together. P(A) = 1, P(B) = 1, then P(AB) = 1 is an example of dependent events because the probability of event A occurring is dependent on the probability of event B occurring.

4. How can P(A) = 1, P(B) = 1, then P(AB) = 1 be proven mathematically?

P(A) = 1, P(B) = 1, then P(AB) = 1 can be proven mathematically using the multiplication rule of probability. This rule states that for two dependent events A and B, the joint probability P(AB) can be calculated by multiplying the individual probabilities P(A) and P(B). In this case, since both P(A) and P(B) are equal to 1, P(AB) will also equal 1.

5. Can P(A) = 1, P(B) = 1, then P(AB) = 1 be applied to real-life situations?

Yes, P(A) = 1, P(B) = 1, then P(AB) = 1 can be applied to real-life situations. For example, if P(A) represents the probability of a person having a fever and P(B) represents the probability of that person having a headache, then P(AB) represents the probability of that person having both a fever and a headache. If both P(A) and P(B) are 1, then P(AB) will also be 1, indicating a high likelihood of a person having both a fever and a headache.

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