Sampling error
In
Since sampling is almost always done to estimate population parameters that are unknown, by definition exact measurement of the sampling errors will not be possible; however they can often be estimated, either by general methods such as bootstrapping, or by specific methods incorporating some assumptions (or guesses) regarding the true population distribution and parameters thereof.
Description
Sampling Error
The sampling error is the
Effective Sampling
In
Even in a perfect non-biased sample, the sample error will still exist due to the remaining statistical component; consider that measuring only two or three individuals and taking the average would produce a wildly varying result each time. The likely size of the sampling error can generally be reduced by taking a larger sample.[3]
Sample Size Determination
The cost of increasing a sample size may be prohibitive in reality. Since the sample error can often be estimated beforehand as a function of the sample size, various methods of sample size determination are used to weigh the predicted accuracy of an estimator against the predicted cost of taking a larger sample.
Bootstrapping and Standard Error
As discussed, a sample statistic, such as an average or percentage, will generally be subject to sample-to-sample variation.[1] By comparing many samples, or splitting a larger sample up into smaller ones (potentially with overlap), the spread of the resulting sample statistics can be used to estimate the standard error on the sample.
In Genetics
The term "sampling error" has also been used in a related but fundamentally different sense in the field of
See also
References
- ^ ISBN 0-387-40620-4
- ISBN 978-1-4557-0736-2.
- ^ Scheuren, Fritz (2005). "What is a Margin of Error?". What is a Survey? (PDF). Washington, D.C.: American Statistical Association. Archived from the original (PDF) on 2013-03-12. Retrieved 2008-01-08.
- ISBN 0-536-68045-0.