Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Optinalytic (Statistical) Mirroring: A New Novel Approach of Measure of Dispersion

Version 1 : Received: 21 November 2019 / Approved: 24 November 2019 / Online: 24 November 2019 (04:53:43 CET)
Version 2 : Received: 12 September 2021 / Approved: 13 September 2021 / Online: 13 September 2021 (13:26:35 CEST)

How to cite: Abdullahi, K.B. Optinalytic (Statistical) Mirroring: A New Novel Approach of Measure of Dispersion. Preprints 2019, 2019110268. Abdullahi, K.B. Optinalytic (Statistical) Mirroring: A New Novel Approach of Measure of Dispersion. Preprints 2019, 2019110268.


The main central goal and statistical power of any statistical tool is to present the reader about the level, degree and strength of variations within or between datasets in a clear and precise interpretation that allows a researcher to make a rational and empirical conclusion. The current tools used for measure of dispersion have been challenged with some limitations. A new novel approach, called optinalytic (statistical) mirroring is proposed in this article to address some of the limitations other measure of dispersion methods have failed to solve and resolve. An optinalytic (statistical) mirrors are designed sequence images on which a sequence or set of sequences can optinalytically and intermetrically reflects to give an inferential information about their comparisons (similarity and dissimilarity). Method validation and comparisons with some most important tools of dispersion measures (e.g: variance, standard deviation, coefficient of variation, variance-to-mean ratio) was established to assess the suitability of the new proposal as an alternative measure of dispersion using different sets of logically generated univariate sequences with different problems and complications. The results of comparison shows that optinalytic (statistical) mirroring is more resistant to extreme outliers, more inferential and works efficiently with negative values with a very meaningful interpretation of result to the common understanding of a non-expert of statistics, which all other methods cannot provide.


statistical mirrors; central tendencies; reflector; reflecter; comparative optinalysis; inferences


Computer Science and Mathematics, Computational Mathematics

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