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Reply #8: Did the icky real world mess up your Gaussian curve? [View All]

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mgr Donating Member (616 posts) Send PM | Profile | Ignore Tue Apr-19-05 02:18 PM
Response to Reply #2
8. Did the icky real world mess up your Gaussian curve?
I think I am the first to ask you if the Central Limit Theorem is what drives this, why are the MOEs higher for states that have larger samples than the national sample, I think you need to conclude that it is a necessary, but not a sufficient explanation.

From above: "Sampling error is affected by the design of the sample, the characteristic being measured and the number of people who have the characteristic. If a characteristic is found in roughly the same proportions in all precincts the sampling error will be lower. If this characteristic is concentrated in a few precincts the sampling error will be larger."

All sampling is constrained by time and space and thus are not perfectly random as mathematics would have it. A state has a smaller spatial extent than a country, and thus has fewer opportunities for a unique characteristic to be sampled. Think gender, and think ethnicity-- gender is can be considered evenly dispersed across the country, whereas African Americans, Jews, Pakistanis, Asian Americans, Hispanic-Americans (redundant) are not, they are clustered. You recall clustering effect don't you?

If you are confused by this, think how likely you are to sample an African American from Utah, and what type of error term would you construct around a sampling event that included one? With the US, you have larger bucket to select from.

What does "Sampling error is affected by the design of the sample,...." mean to you. What is the design of a sample meant to do-- any first year psychology or sociology major will tell you--to reduce bias; and what did NEP report? If there is bias, you cannot assume independence of the various samples from each other, but dependence, which does not allow recalculation of MOE with a larger combined sample size.


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