BACKGROUND The heterogeneity statistic I(2), interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included. METHODS Based on a real meta-analysis, we simulate artificially 'inflating' the sample size under the random effects model. For a given inflation factor M = 1, 2, 3,... and fo...

The effectiveness of fine-needle aspiration (FNA) increases with the number of needle passes, but needle passes are also associated with increased risk of adverse events. The trade-off between needle passes and adequacy has not been well characterized. Clinical studies are limited because of their inherent variability and limited sample size. We developed mathematical models to compare the perf...

In the capture-recapture problem for two independent samples, the traditional estimator, calculated as the product of the two sample sizes divided by the number of sampled subjects appearing commonly in both samples, is well known to be a biased estimator of the population size and have no finite variance under direct or binomial sampling. To alleviate these theoretical limitations, the inverse...

control chart is the most well-known chart to monitor the number of nonconformities per inspection unit where each sample consists of constant size. generally, the design of a control chart requires determination of sample size, sampling interval, and control limits width. optimally selecting these parameters depends on several process parameters, which have been considered from statistical and...

A comparison is made between the variance of the estimator of the total of a variable obtained from both a simple and a stratified random sampling, in which the sample sizes of some strata are equal to the stratum population size. It is shown that in this case, the advantage of the stratified sample could depend on the sample size. The paper presents inequalities that determine, as a function o...

Large sample confidence intervals often have the form Dn ± z1−α/2 SE(Dn) whereDn is an estimator of the parameter and P (Z ≤ zα) = α when Z has a normal N(0,1) distribution. Replacing z1−α/2 by tp,1−α/2 can be viewed as multiplying z1−α/2 SE(Dn) by a finite sample correction factor tp,1−α/2/z1−α/2 in order to improve the performance of the interval for small sample sizes. This technique is used...

Linear systematic sampling design is very simple and operationally convenient in practice, but, when the population size is not a multiple of the sample size, results in a variable sample size and in this case sample mean as an estimator of the population mean is not unbiased. To overcome these difficulties and in some cases to increase the efficiency, a number of modifications of linear system...

inverse sampling design is generally considered to be appropriate technique when the population is divided into two subpopulations, one of which contains only few units. in this paper, we derive the horvitz-thompson estimator for the population mean under inverse sampling designs, where subpopulation sizes are known. we then introduce an alternative unbiased estimator, corresponding to post-str...

In this paper, we propose a variable sample distributed algorithm for the computation of stochastic Nash equilibrium in which the objective functions are replaced, at each iteration, by sample average approximations. We investigate the contraction mapping properties of the variable sample distributed algorithm and show that the accuracy of estimators yielded in the algorithms to their true coun...