Experimental methods, detection criteria

Statistical methods provide an approach that yields quantitative estimates of the random uncertainties in the raw data measurements themselves and also in the conclusions drawn from them. Statistical methods do not detect systematic errors (e.g. bias) present in an assay nor do they give a clear-cut answer to the question as to whether or not a particular experimental result is acceptable. An acceptability criterion must be chosen a priori based on the underlying assumption that the data follow a Gaussian (normal) distribution. A common acceptability criterion is the 95 % confidence level, corresponding to a p-value of 0.05. Because work is with small data sets in trace quantitative analyses, as opposed to the infinitely large data sets required for idealized statistical theory, use is mode of tools and tests based on the (t) distribution (Sudent s t distribution) developed specifically for the statistical analysis of small data sets. 

We have defined a pure substance as one with a fixed composition that is associated with its properties. We usually think of it as being entirely composed of like molecules. However, the most reliable way to detect the presence of foreign molecules in a sample is to separate the foreign matter, and to note that the properties of the new samples differ from those of the old. If attempts at separation il (which is to say that no further change in properties is detected), we may assume that the sample is pure. As far as laboratory operations are concerned, there is no way of showing that the assumption is false except by developing a more effective method to separate the pure substance into new components. In laboratory practice, then, the criterion of purity of a substance is simply the mability of the experimenter to isolate or otherwise detect foreign material. 

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