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Information theory makes it possible to judge and evaluate methods and results in chemical analysis. The obtained information can be expressed in different ways. One way is to define information as the decrease of uncertainty after analysis. Conditional probabilities are therefore considered when evaluating the information provided by qualitative analyses. However, the use of other information measures, such as the information gain, is often preferable. In multicomponent analysis the translation of information from signals to the amounts of the analytes has been investigated along with the relevance of individual components. Information theory can also be applied to find the optimum experimental conditions. The evaluation of the properties of analytical methods by information theory has been proposed.

Negentropy is defined, which characterizes both the precision and the correctness of normally distributed results of quantitative analyses; its properties are discussed and its practical use is proposed in judging and optimizing analytical results.

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