Precision and Accuracy of Analysis

Authored by: Pradyot Patnaik

Handbook of Environmental Analysis

Print publication date:  August  2017
Online publication date:  August  2017

Print ISBN: 9781498745611
eBook ISBN: 9781315151946
Adobe ISBN:


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Statistics is applied broadly in all areas of sciences for a variety of purposes, such as to build models with existing data in order to estimate parameters, select among alternate models, test hypotheses, and make predictions. Its scope generally is broad and interferences are usually determined from comparative measurements. In environmental sciences, its applications also vary. For example, studies related to climatic change often use statistical models to test hypotheses. On the other hand, for field samplings, statistical concepts may be applied to design a specific sampling scheme based on the collected data on the samples, and also the sample “populations,” defined as collection of all the possible observations of interest. However, it may be noted here that any overuse of statistical methods for environmental data analysis may draw very different conclusions if the pitfalls are not identified. There are four statistical methods commonly used in environmental data analysis. The most common of these is the estimation of percentile and confidence interval. It is, however, based on the automatic assumption of a normal distribution to environmental data. However, for heavily contaminated samples, such normal distribution is susceptible to be skewed. The other statistical methods of use are correlation coefficient, regression analysis, and analysis of variance. In the correlation coefficient method, a wide range of data points are used where the data points with maximum values may trivialize other small data points and may consequently skew the correlation coefficient. Similarly, the other two methods which are applied in modeling may have drawbacks too. The regression analysis may give a model which may be more uncertain if the input variables propagate uncertainties. The weakness in the analysis of variance method may be attributed to the acceptance of hypothesis as a weak argument to imply a strong conclusion. The pitfalls of all these methods should be understood and identified. They, however, should not come into play in reference to the trace analyses of pollutants or parameters.

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