title: When is it safer to say nothing? Some considerations on biases in sampling.
abstract: In nursing research, once one has a solid design, one has still to think about-.a sampling strategy and implementation. Too often, the paraphernalia of inferential statistical reasoning is inappropriately deployed when the achieved sample can in no way be claimed to represent the drawn sample. Given the traditionally low rates of response in most nursing research (usually well under 90 per cent, and often unknown), there is a danger that perfectionist counsels would lead to an end to serious research. In this paper, Laurence Moseley and Donna Mead argue that such a nihilistic position is not necessary and that, instead, researchers should tailor their inferential analyses to the demands of any particular study. They argue that for many purposes, simple computations of both maximum and minimum population estimates are both defensible and useful.
authors.affiliation: School of Care Sciences, University of Glamorgan, UK.
authors.forename: Laurence G
authors.initials: LG
authors.lastname: Moseley
authors.forename: Donna M
authors.initials: DM
authors.lastname: Mead
mesh_terms: D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D015399:Nursing Research / Q000379:methods* / Q000592:standards; D010687:Philosophy, Nursing; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards; D018401:Sample Size; D012494:Sampling Studies; D015983:Selection Bias