Probability proof - what formulas are needed here?

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The discussion focuses on proving two probability statements involving events A and B within the same sample space. The first proof demonstrates that if P(A | B') > P(A), then it follows that P(B | A) < P(B). The second proof states that if P(A | B) = P(A), then P(B | A) must equal P(B), but it clarifies that independence cannot be assumed in this context. The participants emphasize using the definitions of conditional probability and independence to derive these conclusions. The conversation highlights the importance of careful reasoning in probability proofs.
SavvyAA3
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If events A and B are in the same sample space:
  • .
Proove that if P(A I B') > P(A) then P(B I A) < P(B)

(where B' is the Probability of A given not B)


  • .
Proove that if P(A I B) = P(A) then P(B I A) = P(B)

do we assume independence here so that P(A I B) = [P(A)*P(B)]/ P(B) = P(A) and state that since P(A n B) = P(B n A) that P(B I A) = [P(B)*P(A)] / P(A) = P(B) or is it wrong to assume independence here?
 
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For the second proof use the fact that P(A|B)=P(A&B)/P(B) and similarly for the other one. You can't assume independence, but it is easy to see that they are using the usual definition of independence P(A&B)=P(A)P(B).
 
Please could you show me the steps you would take
 
SavvyAA3 said:
If events A and B are in the same sample space:
  • .
Proove that if P(A I B') > P(A) then P(B I A) < P(B)

(where B' is the Probability of A given not B)

...

assume
P(A|B&#039;)&gt;P(A)
then

\frac{P(A\cap B&#039;)}{P(B&#039;)}&gt;P(A)

\frac{P(B&#039;|A)P(A)}{P(B&#039;)}&gt;P(A)

\frac{P(B&#039;|A)}{P(B&#039;)}&gt;1

P(B&#039;|A)&gt;P(B&#039;)

1-P(B&#039;|A)&lt;1-P(B&#039;)

P(B|A)&lt;P(B)

the other one isn't much different
 
Thanks soo much!
 
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