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A calibration curve is one approach to the problem of instrument calibration; other standard approaches may mix the standard into the unknown, giving an internal standard. The calibration curve is a plot of how the instrumental response, the so-called analytical signal, changes with the concentration of the analyte (the substance to be measured).
Measurement uncertainty. In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a quantity measured on an interval or ratio scale. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty ...
Definitions. The Lineweaver–Burk plot derives from a transformation of the Michaelis–Menten equation , in which the rate is a function of the substrate concentration and two parameters , the limiting rate, and , the Michaelis constant. Taking reciprocals of both sides of this equation it becomes as follows:
Propagation of uncertainty. In statistics, propagation of uncertainty (or propagation of error) is the effect of variables ' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them. When the variables are the values of experimental measurements they have uncertainties due to measurement ...
Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false negatives that reject the alternative hypothesis when it is true). [a]
Measurement errors can be divided into two ... result of an estimate based upon a ... used to refer to response errors and some other types of non ...
Accuracy measures how close a given set of observations are to their true value. Precision measures how close the observations are to each other. Accuracy and precision of observations lying on a bell curve. In the language of statistics: Accuracy is a description of systematic errors, a measure of bias. Precision is a description of random ...
Type I and type II errors. In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. For example, an innocent person may be convicted. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false.