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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.
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:
In statistical hypothesis testing, the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to ...
In the language of statistics: Accuracy is a description of systematic errors, a measure of bias. Precision is a description of random errors, a measure of variability. In the context of observations made on a ratio or interval scale, a statistical sample can be said to be accurate if its average is close to the true value of the quantity being ...
Measurement errors can be divided into two components: ... The term "observational error" is also sometimes used to refer to response errors and some other types of ...
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 ...
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 ...
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).