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In Smarandache 2014, Florentin introduces neutrosophic statistics but I don't get a core part of it.

He says:

We emphasize, as in neutrosophic probability, that indeterminacy is different from randomness. While classical statistics is referring to randomness only, neutrosophic statistics is referring to both randomness and especially indeterminacy.

He also says:

Neutrosophic Data is the data that contains some indeterminacy.

Something about the indeterminate part of neutrosophic numbers is shown as an interval, although I don't know if there any relation to interval arithmetic here. However, other examples suggest that the indeterminant part is just a measurement of some 'other' category.

What precisely is "indeterminacy" in neutrosophic statistics?

Karolis Koncevičius
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Galen
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    The paper you link to is, frankly, a limited and idiosyncratic effort to recreate parts of "interval arithmetic" developed over the last few decades in the CS and risk analysis literature. Since you are interested in this, consider investigating that literature. The Wikipedia article on p-boxes provides a good entry point. – whuber Nov 28 '21 at 17:29
  • Some related articles can be found here, but this report is especially pedagogical. – Galen Apr 04 '22 at 20:06

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To me this looks like a sensitivity analysis in classical statistics, repackaged to look like a new paradigm. In classical statistics if we have missing [indeterminant] data we could use propensity scores, inverse-probability-weights, single imputation, or multiple imputation to account for these missing [indeterminant] values. Performing several sensitivity analyses would produce a range of values used to account for each missing data point and show how each missing data method impacts the overall conclusions. This paper is suggesting one assigns an interval to each missing value from the very beginning and call it indeterminant statistics. There may be more details but this is what I have gathered from reading the introduction.

Geoffrey Johnson
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