If you put 30 random people in the same room over and over again 1,000 times (different groups of people each time), and then plotted on a graph the times there was at least one match within the group and the times there was not, what would that graph look like?
And the answer is that the percentage of times you would get at least one match should be the percentage predicted by answering the question I posed at 10:01 using the method I proposed.
That’s the difference between probability and distribution. Probability predicts distribution, right? And the question we have in front of us is what is the probability, not what is the distribution.
Wow. Lech, I actually just understood what you wrote at 10:01. And I’m not a math person. That makes sense. Don’t know if it’s right or not, but it makes sense the way you wrote it. (why couldn’t my math teachers in school speak so simply???) Can you explain as simply what others were trying to say?
Speaking of probabilities…what is the chance that I’m sleeping with a guy on the west coast and at the same time, my partner on the east coast meets and sleeps with a friend of the guy I’m with on the west coast but who is actually from Florida and the two of them don’t know about it????
M4L, I was also thinking about people whose diamond jewelry enabled them to escape countries–that’s how my mother’s family got out of Germany in 1939, my grandmother had something valuable that she could hide and then sell for getaway money.
I disagree that what you’re looking for is a probability distribution. The question is:
“What is the likelihood that in a room full of X number of people at least two people will share a birthday.”
This is a single probability for a binary question (either there is at least one shared birthday or there is not), not a distribution.
The probability of at least one match is the inverse of the probability that there are no matches.
And the way to solve the problem is to ask, as each person is added to the group, “what is the likelihood that this person will NOT share a birthday with someone who is already in the group?” Then multiply that probability by the cumulative probability of getting to the last person before him without a match, and so on and so on.
When you get to the 366th person, the probability of no match is zero, so multiply that by the cumulative probability of no match (at this point vanishingly small) and you get zero chance of no match, the inverse of which is 100% chance of at least one match.
quote:
What does gay marriage have to do with your views on monogamy? Are you suggesting heterosexuals are more faithful than individuals in the LGBT community?
yes. well not lesbians, lesbians can be pretty monogymous. gay guys? uh, pretty much never.
Oy. kinda lost me there.
A distribution would be:
If you put 30 random people in the same room over and over again 1,000 times (different groups of people each time), and then plotted on a graph the times there was at least one match within the group and the times there was not, what would that graph look like?
And the answer is that the percentage of times you would get at least one match should be the percentage predicted by answering the question I posed at 10:01 using the method I proposed.
That’s the difference between probability and distribution. Probability predicts distribution, right? And the question we have in front of us is what is the probability, not what is the distribution.
“based on my small sample size of the LGBT community (DIBS) – yes”
Touché, DH
(accent aigu added for Lech’s amusement)
I attended 2 weddings in California for same sex couple who had been together for more than 10 years.
Wow. Lech, I actually just understood what you wrote at 10:01. And I’m not a math person. That makes sense. Don’t know if it’s right or not, but it makes sense the way you wrote it. (why couldn’t my math teachers in school speak so simply???) Can you explain as simply what others were trying to say?
Speaking of probabilities…what is the chance that I’m sleeping with a guy on the west coast and at the same time, my partner on the east coast meets and sleeps with a friend of the guy I’m with on the west coast but who is actually from Florida and the two of them don’t know about it????
M4L, I was also thinking about people whose diamond jewelry enabled them to escape countries–that’s how my mother’s family got out of Germany in 1939, my grandmother had something valuable that she could hide and then sell for getaway money.
Oh, and benson, back to last night’s topic:
I disagree that what you’re looking for is a probability distribution. The question is:
“What is the likelihood that in a room full of X number of people at least two people will share a birthday.”
This is a single probability for a binary question (either there is at least one shared birthday or there is not), not a distribution.
The probability of at least one match is the inverse of the probability that there are no matches.
And the way to solve the problem is to ask, as each person is added to the group, “what is the likelihood that this person will NOT share a birthday with someone who is already in the group?” Then multiply that probability by the cumulative probability of getting to the last person before him without a match, and so on and so on.
When you get to the 366th person, the probability of no match is zero, so multiply that by the cumulative probability of no match (at this point vanishingly small) and you get zero chance of no match, the inverse of which is 100% chance of at least one match.
quote:
What does gay marriage have to do with your views on monogamy? Are you suggesting heterosexuals are more faithful than individuals in the LGBT community?
yes. well not lesbians, lesbians can be pretty monogymous. gay guys? uh, pretty much never.
*rob*