370: Sample Size Determination for the Regression Estimate of the Mean

Biometrics ◽  
1974 ◽  
Vol 30 (4) ◽  
pp. 709 ◽  
Author(s):  
Aaron Tenenbein
Keyword(s):  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Regression Estimate ◽  
The Mean

scholarly journals Optimum sample size to estimate mean parasite abundance in fish parasite surveys

2018 ◽  
Vol 55 (1) ◽  
pp. 52-59 ◽  
Author(s):  
S. Shvydka ◽  
V. Sarabeev ◽  
V. D. Estruch ◽  
C. Cadarso-Suárez
Keyword(s):  
Sample Size ◽  
Sample Size Determination ◽  
Superior Performance ◽  
Size Determination ◽  
Optimum Sample Size ◽  
True Value ◽  
Wide Range ◽  
Precision Level ◽  
The Mean ◽  
Optimum Sample

Summary To reach ethically and scientifically valid mean abundance values in parasitological and epidemiological studies this paper considers analytic and simulation approaches for sample size determination. The sample size estimation was carried out by applying mathematical formula with predetermined precision level and parameter of the negative binomial distribution estimated from the empirical data. A simulation approach to optimum sample size determination aimed at the estimation of true value of the mean abundance and its confidence interval (CI) was based on the Bag of Little Bootstraps (BLB). The abundance of two species of monogenean parasites Ligophorus cephali and L. mediterraneus from Mugil cephalus across the Azov-Black Seas localities were subjected to the analysis. The dispersion pattern of both helminth species could be characterized as a highly aggregated distribution with the variance being substantially larger than the mean abundance. The holistic approach applied here offers a wide range of appropriate methods in searching for the optimum sample size and the understanding about the expected precision level of the mean. Given the superior performance of the BLB relative to formulae with its few assumptions, the bootstrap procedure is the preferred method. Two important assessments were performed in the present study: i) based on CIs width a reasonable precision level for the mean abundance in parasitological surveys of Ligophorus spp. could be chosen between 0.8 and 0.5 with 1.6 and 1x mean of the CIs width, and ii) the sample size equal 80 or more host individuals allows accurate and precise estimation of mean abundance. Meanwhile for the host sample size in range between 25 and 40 individuals, the median estimates showed minimal bias but the sampling distribution skewed to the low values; a sample size of 10 host individuals yielded to unreliable estimates.

Confidence Interval Construction by Stein's Method: A Practical and Economical Approach to Sample Size Determination

1989 ◽  
Vol 26 (2) ◽  
pp. 237-240 ◽  
Author(s):  
Raphael Gillett
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Correction Factors ◽  
Mean And Variance ◽  
The Mean ◽  
Sound Theoretical Basis ◽  
Straightforward Approach ◽  
Economical Choice

A two-stage method of confidence interval construction developed by Stein is a practical and straightforward approach to sample size determination when one wants to estimate the mean of a normal distribution of unknown mean and variance. The technique has several advantages over the modified version of current informal practice proposed by Shiffler and Adams in a recent JMR article: (1) the need for correction factors is eliminated, (2) the precision of the confidence interval is fixed rather than uncertain and the desired level of confidence is guaranteed, irrespective of the unknown population variance, and (3) a sound theoretical basis is provided for making an economical choice of sample size.

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