The use of nonparametric statistics in quantitative electron microscopy.
Parametric statistical methods assume samples that have a normal distribution and representative sample sizes (i.e. n >20). Quantitative electron microscopy is inherently restricted to small sample sizes and a priori there is no way to know if the expression of the ligand being studied has a normal distribution. Thus to make statistical inferences based on data generated by quantitative electron microscopy using parametric methods may not be justified. Nonparametric statistical methods offer a tool for the evaluation of data that do not meet the criteria for analysis by parametric methods. In this report I show the utility of using nonparametric statistical methods for the analysis of data generated by quantitative electron microscopy.
Morris
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Department of Cell Biology, Neurobiology and Anatomy, University of Cincinnati, College of Medicine, OH 45267-0521, USA. Randal.Morris@UC.Edu
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D001427:Bacterial Toxins; D011510:Proteolipids; D037661:Pulmonary Surfactant-Associated Proteins; D011663:Pulmonary Surfactants; D011956:Receptors, Cell Surface