Statistical benefit

Statistical benefit Using statistical methods in the design of experiments and data analy-... 

There was limited statistical benefit from three lots. 

There should be flexibility in the way a standard is applied to reflect the levels of uncertainty associated with the standard and the consequences of failure. If the consequences of failure are controversial or expensive, it makes sense to demand that failure is demonstrated with high confidence before such action is imposed on the unwilling. This gives the statistical benefit of the doubt to the polluter. If less dramatic action is to be imposed (e.g., a requirement for further studies), we might act on less confidence of failure. Costly action must not be imposed solely because a regulator has chosen not to monitor adequately. 

Figure 2-11. Potential frequency of problems without benefit of modern improvements, statistically related to inlet pressure (1 atm = 101.3 kNm ).

One major oil and chemical company has collected data on the cost of accidents, the equipment involved and the cause of the failure for more than 50 years. These data are analyzed annually to help decide where to focus efforts to reduce losses and/or to modify design standards to prevent recurrence. This analysis also identifies failures of the PSM and ESH management system. These can be compared with the cost of delivering the systems and adjustments made to expenditures to improve the cost/benefit balance. Any such changes must be carefully considered as normal statistical variation may cause you to take unjustified action. 

A key element in planning and conducting clinical trials is to ensure that they have scientific validity and objectivity. This is particularly relevant with respect to Phase II and III studies, where it is desired to demonstrate a positive benefit to risk outcome. Responses to a drug among a patient population are rarely homogeneous and clear-cut. Thus, sound statistical principles must be applied in order to be able to distinguish significant effects from random events. 

Statistical measures of changes in line with the mentoring objectives (e.g. alterations in productivity or staff turnover, cost-benefit analyses). 

Chapters 1 and 2 introduced the basic statistical tools. The necessary computer can do more than just run statistics packages in this chapter, a number of techniques are explained that tap the benefits of fast data handling, namely filtering, optimization, and simulation. 

However, there are a number of issues here. In the first place, stress itself is a somewhat nebulous concept, and there is continuing debate about how it should be defined. Second, even with the benefit of multivariate statistics and the techniques of bioinformatics, measuring stress from all sources in a meaningful way is dauntingly complex and may not be realizable in practice. 

The larger the benefit, the smaller the number of subjects required to show statistical significance. For example, if a herbal supplement is expected to produce a 5% lowering of the blood cholesterol level vs placebo, this will require many more subjects than a study evaluating the effects of a drug that is expected to produce a 50% reduction vs placebo. 

EWLS regression is by statistical definition the optimum method, and has the added benefits that (a) uncertainties of the slope and intercept of the regression are easily estimated, and (b) the probability that the data support assumption (1) above can be readily calculated. 

It was shown by Englezos et al. (1998) that use of the entire database can be a stringent test of the correlational ability of the EoS and/or the mixing rules. An additional benefit of using all types of phase equilibrium data in the parameter estimation database is the fact that the statistical properties of the estimated parameter values are usually improved in terms of their standard deviation. 

---