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There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]
A calibration curve plot showing limit of detection (LOD), limit of quantification (LOQ), dynamic range, and limit of linearity (LOL).. In analytical chemistry, a calibration curve, also known as a standard curve, is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration. [1]
The formal definition of calibration by the International Bureau of Weights and Measures (BIPM) is the following: "Operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties (of the calibrated instrument or ...
Calibration training is used to increase a person’s ability to provide accurate estimates for stochastic methods. Research found that most people could be calibrated if they took the time and that a person’s calibration i.e. performance in providing accurate estimates, carries over to estimates provided for content outside of the ...
[3] [4] The most common approach for accounting for matrix effects is to build a calibration curve using standard samples with known analyte concentration and which try to approximate the matrix of the sample as much as possible. [2] This is especially important for solid samples where there is a strong matrix influence. [5]
Oversampling or undersampling as well as assigning weights to samples is an implicit way to find a certain pareto optimum (and it sacrifices the calibration of the estimated probabilities). A more explicit way than oversampling or downsampling could be to select a Pareto optimum by