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If each individual poll has a margin of error, when you combine all the polls, why do they say poll of polls has no margin of error? Shouldn't the margin of error get multiplied when you combine many polls?
Who is they?why do they say
Because "they" refers in this case to people who don't know what they are talking about.If each individual poll has a margin of error, when you combine all the polls, why do they say poll of polls has no margin of error? Shouldn't the margin of error get multiplied when you combine many polls?
EDIT: Not sure if it is all media or just cnn.The CNN Poll of Polls, which does not have a margin of error, includes the USA Today/Suffolk poll; the ABC News Poll conducted October 21-24; the CNN/ORC poll conducted October 20-23; the Quinnipiac University poll conducted October 17-18; and the Fox News poll conducted October 22-25.
That doesn't make it right.Pretty much all US media says that.
I interpret that as "due to constraints error can't be estimated" not "we're perfect."Pretty much all US media says that. As an example,
http://edition.cnn.com/2016/10/26/politics/cnn-poll-of-polls-october/
EDIT: Not sure if it is all media or just cnn.
Care to elaborate a bit more? Are you implying the margin of error should get multiplied and cnn is incorrectly and ignorantly stating that there is no margin of error.That doesn't make it right.
So did I, which is why I wanted him to tell us where he got this from.I interpreted the OP's original statement "why do they say poll of polls has no margin of error" as meaning there was no error.
I wonder if that's true. Chebyshev's inequality gives some upper limit to the variance (with very weak assumptions). Perhaps some analogous model could be used for a case where you mix data from different sources.You cannot lump data from many disparate methodologies into one amorphous blob and make sense of it.
How does Chebyshev help at all??I wonder if that's true. Chebyshev's inequality gives some upper limit to the variance (with very weak assumptions). Perhaps some analogous model could be used for a case where you mix data from different sources.
Could be the upper limit would be so huge it is not worth calculating though.
Yes, and I would expect the result to have a narrower error margin, even if it is tough to quantify."Poll of polls?" Isn't that on a par with combining the forecast tracks for hurricanes?
I am not saying it helps directly. I just wonder if there is no similar inequality that would say something about the upper limit of errors for the poll of polls.How does Chebyshev help at all??
IIRC, only in the case that the polls are i) statistically independent, ii) the errors Gaussian. A big IF.It is possible to combine the results of individual polls to obtain a meta-poll. It's also possible to calculate a margin of error for that meta-poll, and that margin of error will have a expected, general trend to be 1√N1N \frac{1}{\sqrt{N}} of that of an individual poll,
The errors do not have to be Gaussian. They can be of any probability distribution that you can think up (assuming the standard deviations are finite). The sum will approach being Gaussian, but that is not a requirement of the individual trials. That's part of the beauty of the Central Limit Theorem.IIRC, only in the case that the polls are i) statistically independent, ii) the errors Gaussian. A big IF.
Is it not? Assuming that we're careful on how percentage and percentage points are used here, I think your method of combining is correct.Also, what do you do if you get one poll that measures 40 +/- 1% and the other that measures 60 +/- 1%? I don't think 50 +/- 0.7% is the right answer.