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The term "sampling error" has also been used in a related but fundamentally different sense in the field of genetics; for example in the bottleneck effect or founder effect, when natural disasters or migrations dramatically reduce the size of a population, resulting in a smaller population that may or may not fairly represent the original one.
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power .
Where is the sample size, = / is the fraction of the sample from the population, () is the (squared) finite population correction (FPC), is the unbiassed sample variance, and (¯) is some estimator of the variance of the mean under the sampling design. The issue with the above formula is that it is extremely rare to be able to directly estimate ...
At a later date, another sample is then taken from the population (re-capture), and the proportion of previously marked samples is used to estimate the actual population size. This method can be extended to determining the validity of a sampling frame by taking a sample directly from the target population and then taking another sample from the ...
Pollsters don't have that kind of time — or money — so they use smaller samples of the population. They seek to identify representative samples in which all members of the larger group have a ...
Denoting the number of events by X, we therefore wish to find the values of the parameter p of a binomial distribution that give Pr(X = 0) ≤ 0.05. The rule can then be derived [ 2 ] either from the Poisson approximation to the binomial distribution , or from the formula (1− p ) n for the probability of zero events in the binomial distribution.
Generally Bessel's correction is an approach to reduce the bias due to finite sample size. Such finite-sample bias correction is also needed for other estimates like skew and kurtosis, but in these the inaccuracies are often significantly larger. To fully remove such bias it is necessary to do a more complex multi-parameter estimation.