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To determine an appropriate sample size n for estimating proportions, the equation below can be solved, where W represents the desired width of the confidence interval. The resulting sample size formula, is often applied with a conservative estimate of p (e.g., 0.5): = /
The Neyman allocation formula is: = where: n h is the sample size for stratum h; n is the total sample size; N h is the population size for stratum h; S h is the standard deviation of the variable of interest in stratum h; Σ represents the sum over all strata
This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes.
The simplest bootstrap method involves taking the original data set of heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N. The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and ...
According to this formula, the power increases with the values of the effect size and the sample size n, and reduces with increasing variability . In the trivial case of zero effect size, power is at a minimum ( infimum ) and equal to the significance level of the test α , {\displaystyle \alpha \,,} in this example 0.05.
Similarly, for a sample of size n, the n th order statistic (or largest order statistic) is the maximum, that is, = {, …,}. The sample range is the difference between the maximum and minimum. It is a function of the order statistics:
where n is the sample size and N is the population size. A sampling fraction value close to 1 will occur if the sample size is relatively close to the population size. When sampling from a finite population without replacement, this may cause dependence between individual samples. To correct for this dependence when calculating the sample ...
Correction factor versus sample size n.. When the random variable is normally distributed, a minor correction exists to eliminate the bias.To derive the correction, note that for normally distributed X, Cochran's theorem implies that () / has a chi square distribution with degrees of freedom and thus its square root, / has a chi distribution with degrees of freedom.