If mutually exclusive, prove Pr(A) <= Pr(B')

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In summary, the original problem is easy to solve, but the solution is not as straightforward as it could be.
  • #1
bjersey
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I'm having issues proving the following which should be simple:

If A and B are mutually exclusive, prove Pr(A) <= Pr(B')

From the statement about being mutually exclusive, I know A [tex]\cap[/tex] B = [tex]\phi[/tex]

Therefore we have P(A [tex]\cap[/tex] B) = Pr(A) + Pr(B)

Also, A = A [tex]\cap[/tex] B'
and B = A' [tex]\cap[/tex] B

But I'm having a hard time putting all of this together.

Please help. Thanks.
 
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  • #2
bjersey said:
I'm having issues proving the following which should be simple:

If A and B are mutually exclusive, prove Pr(A) <= Pr(B')

From the statement about being mutually exclusive, I know A [tex]\cap[/tex] B = [tex]\phi[/tex]

Therefore we have P(A [tex]\cap[/tex] B) = Pr(A) + Pr(B)

Also, A = A [tex]\cap[/tex] B'
and B = A' [tex]\cap[/tex] B

But I'm having a hard time putting all of this together.

Please help. Thanks.
A = A [tex]\cap[/tex] B' implies A is a subset of B'.
 
  • #3
bjersey said:
I'm having issues proving the following which should be simple:

If A and B are mutually exclusive, prove Pr(A) <= Pr(B')

From the statement about being mutually exclusive, I know A [tex]\cap[/tex] B = [tex]\phi[/tex]

Therefore we have P(A [tex]\cap[/tex] B) = Pr(A) + Pr(B)

Also, A = A [tex]\cap[/tex] B'
and B = A' [tex]\cap[/tex] B

But I'm having a hard time putting all of this together.

Please help. Thanks.

Here's my solution.

Pr(A union B) <= Pr(Entire Sample Space)=1 from probability axiom; and
Pr(A union B) = Pr(A)+Pr(B)-Pr(A intersection B)

Hence, Pr(A intersection B) >= Pr(A) + Pr(B) -1 ...(3)

But for two mutually exclusive events Pr(A intersection B) = P(empty) = 0. Also, Pr(B) = 1- Pr(B')

It follows from (3) that

0>=Pr(A)+1-Pr(B')-1

Therefore, Pr(B')>=Pr(A)

As required
 
  • #4
You could cut the final proof down a little:

[tex]
\begin{align*}
P(A) + P(B) - P(A \cap B) & \le 1 \\
P(A) + P(B) & \le 1 \\
P(A) & \le 1 - P(B) \\
P(A) & \le P(B')
\end{align*}
[/tex]
 
  • #5
statdad said:
You could cut the final proof down a little:

[tex]
\begin{align*}
P(A) + P(B) - P(A \cap B) & \le 1 \\
P(A) + P(B) & \le 1 \\
P(A) & \le 1 - P(B) \\
P(A) & \le P(B')
\end{align*}
[/tex]

The inequality in (3) is well known named after Bonferonni. It has many applications. Its derivation is good to know too. Anyway, your answer is neat.
 
  • #6
Yes, Bonferroni's inequality is important (classical example is in the first study of multiple comparisons), but the original question wasn't about that; usually you want the derivations to be as straightforward as possible.

there is nothing wrong with the earlier solution, but the mix of mathematics and english makes its reading awkward. learning when the written word can be safely removed from the mathematical work is an important step as well.
 
  • #7
I think my comment is the simplest. A is a subset of B', therefore P(A) ≤ P(B').
 
  • #8
mathman, there is no doubt about that, and if i implied anything else, my apologies. my point was meant as an add-on to the longer approach give by others.
 

FAQ: If mutually exclusive, prove Pr(A) <= Pr(B')

What does it mean for two events to be mutually exclusive?

Mutually exclusive events are events that cannot occur at the same time. This means that if one event happens, the other event cannot happen.

How can we prove that Pr(A) is less than or equal to Pr(B') if the events are mutually exclusive?

We can prove this by using the fundamental rule of probability, which states that for mutually exclusive events, the probability of either event occurring is equal to the sum of their individual probabilities. Since the probability of B' occurring is equal to 1 minus the probability of B occurring, and A and B are mutually exclusive, the probability of A occurring must be less than or equal to the probability of B' occurring.

Can you provide an example to illustrate this concept?

Sure! Let's say we have two events, A and B, where A represents the event of getting heads on a coin toss and B represents the event of getting tails on a coin toss. These events are mutually exclusive because it is impossible to get both heads and tails on the same coin toss. In this case, the probability of A is 0.5 (50%) and the probability of B' (getting anything other than tails) is also 0.5. Therefore, Pr(A) is equal to Pr(B').

Does this rule only apply to coin tosses or can it be applied to other scenarios?

This rule can be applied to any scenario where the events are mutually exclusive. For example, it can be applied to events such as rolling a 6 on a die and rolling any other number, or picking a red marble from a bag and picking a blue marble from the same bag.

How can knowing this concept be useful in science?

In science, we often use probability to make predictions and decisions. Understanding the concept of mutually exclusive events and how it relates to probability can help us accurately calculate and interpret the likelihood of certain outcomes. This is especially important in fields such as biology, where we may need to determine the probability of certain genetic traits being passed down from parents to offspring.

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