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The Westgard rules are a set of statistical patterns, each being unlikely to occur by random variability, thereby raising a suspicion of faulty accuracy or precision of the measurement system. They are used for laboratory quality control, in "runs" consisting of measurements of multiple samples
The Performance Test Standard PTC 19.1-2005 "Test Uncertainty", published by the American Society of Mechanical Engineers (ASME), discusses systematic and random errors in considerable detail. In fact, it conceptualizes its basic uncertainty categories in these terms.
Essentially, the mean is the location of the PDF on the real number line, and the variance is a description of the scatter or dispersion or width of the PDF. To illustrate, Figure 1 shows the so-called Normal PDF , which will be assumed to be the distribution of the observed time periods in the pendulum experiment.
For a Type I error, it is shown as α (alpha) and is known as the size of the test and is 1 minus the specificity of the test. This quantity is sometimes referred to as the confidence of the test, or the level of significance (LOS) of the test. For a Type II error, it is shown as β (beta) and is 1 minus the power or 1 minus the sensitivity of ...
Since the announcement of Pons and Fleischmann in 1989, cold fusion has been considered to be an example of a pathological science. [15] Two panels convened by the US Department of Energy , one in 1989 and a second in 2004, did not recommend a dedicated federal program for cold fusion research. [ 16 ]
The statistical errors, on the other hand, are independent, and their sum within the random sample is almost surely not zero. One can standardize statistical errors (especially of a normal distribution ) in a z-score (or "standard score"), and standardize residuals in a t -statistic , or more generally studentized residuals .
If the users know the amount of the systematic error, they may decide to adjust for it manually rather than having the instrument expensively adjusted to eliminate the error: e.g. in the above example they might manually reduce all the values read by about 4.8%.
In statistical hypothesis testing, there are various notions of so-called type III errors (or errors of the third kind), and sometimes type IV errors or higher, by analogy with the type I and type II errors of Jerzy Neyman and Egon Pearson. Fundamentally, type III errors occur when researchers provide the right answer to the wrong question, i.e ...