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#### Introduction to Neutrosophic Statistics

Autori: Florentin Smarandache

Editorial: p.125, 2014.

Rezumat:

Neutrosophic Statistics is an extension of the classical statistics. While in classical statistics the data is known, formed by crisp numbers, in neutrosophic statistics the data has some indeterminacy. In the neutrosophic statistics, the data may be ambiguous, vague, imprecise, incomplete, even unknown. Instead of crisp numbers used in classical statistics, one uses sets (that respectively approximate these crisp numbers) in neutrosophic statistics. Also, in neutrosophic statistics the sample size may not be exactly known (for example the sample size could be between 90 and 100; this may happen because, for example, the statistician is not sure about 10 sample individuals if they belong or not to the population of interest; or because the 10 sample individuals only partially belong to the population of interest, while partially they don’t belong). In this example, the neutrosophic sample size is taken as an interval n = [90, 100], instead of a crisp number n = 90 (or n = 100) as in classical statistics.

Neutrosophic Statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. In Classical Statistics all data are determined; this is the distinction between neutrosophic statistics and classical statistics. In many cases, when indeterminacy is zero, neutrosophic statistics coincides with classical statistics. We can use the neutrosophic measure for measuring the indeterminate data. The neutrosophic statistical methods will enable us to interpret and organize the neutrosophic data (data that may have some indeterminacies) in order to reveal underlying patterns.