Your box of cereal may be a contest winner! It's rattling, which 100% of winning boxes do. Of course 1% of all boxes rattle and only one box in a million is a winner. What is the probability that your box is a winner?
What is the probability of the box?
6 Answers
The correct solution would be $0.0001$ ($1/10000$), wouldn't it? It's late, but it seems to me that Drew Christianson miscalculated and dedocu mixed $p(A)$ and $p(B)$ - correct me please, if I'm wrong.
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0Sorry for blaming you - I just saw that the downvote happened basically at the same minute you made the correction and I was just wondering ... – 2017-12-06
HINT: Suppose that there are a million boxes. One of them is a winner, and $10~000$ of them rattle. So the probability that a box that rattles is a winner is ... ?
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0I was going to post something like this myself. A good way to approach these problems is to just make up a population size—in this case 1,000,000—and then compute the sizes of the sub-populations. This concrete approach is often easier to reason about than the corresponding (and mathematically equivalent) probabilities. – 2012-09-24
Bayes Rule! : $ P(A|B) = \frac{P(B|A)P(A)}{P(B)}$ You want $P(A|B)$ where $A$ is the box being a winner and $B$ is the box rattling. We know winning boxes rattle and thus $P(B|A)=1$. You have $P(A)=\frac{1}{1000000}$ and $P(B)= \frac{1}{100}$. Thus: $P(A|B) = \frac{1*\frac{1}{1000000}}{\frac{1}{100}} = \frac{100}{1000000} = .0001$
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0Definitely, but I think it's also useful to do some easy problems with more complicated machinery, just to see how things work. – 2012-09-24
On average, out of every 1,000,000 boxes, $\frac{1000000}{100}=$10,000 boxes rattle and $\frac{1000000}{1000000}=$1 box wins.
Divide the ultimate number by the penultimate number.
This is a simple example for Bayes' theorem:
Denote as A the event when the box is rattling and as B the event when the box is a winner. We then have:
$p(A) = 0.01$
and
$p(B) = 1 * 10^{-6}.$
Further, we know that all the boxes that are winners rattle, which writes:
$p(A|B) = 1,$ which means the probability of rattling knowing that the box is a winning box. Now, according to Bayes' theorem we have: $p(B|A) = \frac{p(A|B)p(B)}{p(A)} = \frac{p(B)}{p(A)},$ which gives you the probability of winning, knowing that the box rattles: $p(B|A) = 0.0001$
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0Corrected after suggestion from Christian...there was a $0$ to much in the result...sorry for that! – 2012-09-26
Note that $p(A \cap B) = p(B),$ since every winning box rattles. Therefore according to the definition of conditional probabilities, we have:
$p(B|A) = \frac{p(A \cap B)}{p(A)} = \frac{p(B)}{p(A)},$ and we find the same result as when using Bayes' theorem.