This paper addresses the problem of estimating a binomial proportion from several independent samples in agricultural research, where the arithmetic average is widely used. The penalties of using a suboptimal estimator, the arithmetic estimator, relative to the preferred best estimator, the weighted average, are theoretically and empirically investigated, using numerical illustrations and simulation studies. Raw count data from a study of the proportion of inoculated transgenic hairy roots expressing resistance to cyst nematode in soybean (Glycine max) cultivars and a set of 10 examples of proportion estimation involving several independent samples are used for a practical evaluation of the findings. Results show that using the arithmetic average estimator can inflate variance and widen large sample confidence intervals of the estimates. The weighted average is recommended.
|Number of pages||10|
|Journal||New Zealand Journal of Crop and Horticultural Science|
|Publication status||Published - Sep 2005|