Statistical design conceptual

It should be recognized that the total volume of wastewater as well as the chemical analyses iadicating the organic and inorganic components are requited, backed by statistical validity, before the conceptualizing of the overall treatment plant design can begia. The basic parameters ia wastewater characterization are summarized ia Table 2. 

The analysis of the mean response and the s/n ratio can be performed employing the usual ANOVA and/or hypothesis tests to detect which factors or interactions have statistical significance. Taguchi proposed a conceptual approach based on the graphical display of the effects (they are called factor plots or marginal means followed by a qualitative evaluation. This provides objective information and a test for the significance of each design factor on the two observed responses mean and s/n ratio. 

Unfortunately, it is impossible to design an experiment that will totally disprove a theory based on random phenomena. Various outcomes may occur, some of which may be unlikely but not impossible. Thus Popper s falsifiability condition does not apply. The statistical method advocated by Fisher (1956) attempts to overcome this problem by substituting unlikely for impossible but otherwise follows the principles of the scientific method. With this substitution, Fisher and others proposed conceptual structures for testing theories and scientific hypotheses under conditions of uncertainty that are analogous to the scientific method. However, these approaches, although being very useful in practice, have raised a host of conceptual issues that are the subject of ongoing debates. 

In this chapter we highlight additional statistical considerations relevant to therapeutic confirmatory trials, and other study designs that also provide important information upon which to base decision-making. These additional insights and information build upon the material presented so far. As this chapter is largely conceptual rather than computational, we have included a number of references to guide your further reading. 

Nurrenbem and Pickering s study was not designed to examine these whys and hows. Rather, the quantitative design of their research study provided convincing statistical evidence that a problem existed in the first place, i.e., that a difference between algorithmic and conceptual problem solving existed. 

Due to insufficient knowledge of core characteristics and data at the conceptual design stage, the uncertainties in reactivity components could be large, especially as comes to calculated core parameters. In setting a shutdown reactivity requirement, it is prudent to include these large uncertainties to assure that the absorbers will have an ample worth. The total uncertainty is obtained by statistically combining all uncertainties. 

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