Statistical thinking is practice : handling variability in experimental situations
By: BJERKE, Froydis
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Material type: 
ArticlePublisher: Oxfordshire : Routledge, November 2002Total Quality Management 13, 7, p. 1001-1014Abstract: In all experimental situations, handling variability properly is important in order to achieve reliable and informative experimental results. The benefits from `design of experiments' are well known to most experimentation, but this article emphasizes the more practical matters of planning and performing experiments. Poor control of the variation sources in the experimental situation will generally lead to poor results. Three important statistical tools for handling variability in experiments are presented- replication, blocking and randomization. These tools are discussed in examples of real experimetns, to illustrate the practical aspects os statistical thinking. In this context, statistical thinking may be viewe as utilizing common sense before it becomes belated wisdom. The illustrative examples may be viewed as failures, and not worthy of publishing, but in this context they are useful for illustrating commonly occurring obstacles and mistakes in experimental work
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Periódico | Biblioteca Graciliano Ramos | Periódico | Not for loan |
In all experimental situations, handling variability properly is important in order to achieve reliable and informative experimental results. The benefits from `design of experiments' are well known to most experimentation, but this article emphasizes the more practical matters of planning and performing experiments. Poor control of the variation sources in the experimental situation will generally lead to poor results. Three important statistical tools for handling variability in experiments are presented- replication, blocking and randomization. These tools are discussed in examples of real experimetns, to illustrate the practical aspects os statistical thinking. In this context, statistical thinking may be viewe as utilizing common sense before it becomes belated wisdom. The illustrative examples may be viewed as failures, and not worthy of publishing, but in this context they are useful for illustrating commonly occurring obstacles and mistakes in experimental work
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