Does Conditional Probability Increase with Dependence?

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In summary, when $P(A|B)>P(A)$ and $P(A)=P(B)=\frac{2}{3}$, then $P(B|A)>P(B)$ and the first option is true.
  • #1
mathmari
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Hey! :eek:

Suppose that for the events $A,B$ of an experiment it holds that $P(A|B)>P(A)$ ( $P(A)=P(B)=\frac{2}{3}$ ) then what of the following holds?

  1. $P(B|A)>P(B)$ ( $P(A|B)\geq \frac{1}{2}$ )
  2. $P(B|A)>P(B)$ ( $P(A|B)\leq \frac{1}{3}$ )
  3. $P(B|A)<P(B)$ ( $P(A|B)\geq \frac{1}{3}$ )
  4. $P(B|A)<P(B)$ ( $P(A|B)\geq \frac{1}{6}$ )
I have done the following:

$P(A|B)=\frac{P(BA)}{P(B)}$ and since $P(A|B)>P(A) \Rightarrow \frac{P(BA)}{P(B)}>P(A) \Rightarrow P(BA)>P(B)P(A)$.

Then $P(B|A)=\frac{P(AB)}{P(A)}>\frac{P(B)P(A)}{P(A)}=P(B)$, right? (Wondering)

When $P(A)=P(B)=\frac{2}{3}$, then $P(A|B)>P(A)\Rightarrow P(A|B)>\frac{2}{3}$.

How can we continue?
 
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  • #2
Hey mathmari! (Smile)

Don't we already have enough information to figure out which answers are true or false? (Wondering)
 
  • #3
I like Serena said:
Hey mathmari! (Smile)

Don't we already have enough information to figure out which answers are true or false? (Wondering)

(Thinking)

The first one is correct, or not? (Wondering)
 
  • #4
mathmari said:
(Thinking)

The first one is correct, or not? (Wondering)

Hmm... let's see...

We found that $P(A|B) > \frac 23$.
Does that imply that $P(A|B) \ge \frac 12$ or not? (Wondering)
 
  • #5
In addition to I Like Serena's post, making use of Bayes rule:
$$P(B \ | \ A) = \frac{P(A \ | \ B) P(B)}{P(A)} = P(A \ | \ B) > P(A) = P(B).$$
Hence, first answer is indeed correct.
 

FAQ: Does Conditional Probability Increase with Dependence?

What does the notation $P(B|A)$ mean?

The notation $P(B|A)$ represents the conditional probability of event B occurring given that event A has already occurred.

What does it mean if $P(B|A)>P(B)$?

If $P(B|A)>P(B)$, it means that the probability of event B occurring, given that event A has occurred, is greater than the probability of event B occurring on its own.

How do you interpret the statement "Given that A has occurred, the probability of B occurring is greater than the probability of B occurring on its own"?

This statement means that the occurrence of event A has an impact on the probability of event B occurring. In other words, event A is influencing the likelihood of event B happening.

What are some real-world examples of $P(B|A)>P(B)$?

One example could be that the probability of getting into a car accident is higher if it is raining (event A), compared to the probability of getting into a car accident on a sunny day (event B). Another example could be that the probability of winning a game is higher if a specific player is on your team (event A), compared to the probability of winning without that player (event B).

Is $P(B|A)>P(B)$ always true?

No, $P(B|A)>P(B)$ is not always true. It only holds true when event A has an impact on the likelihood of event B occurring. If event A has no influence on the probability of event B, then $P(B|A)$ will be equal to $P(B)$.

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