Authors : Sujay Rao Mandavilli

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/4pj4aj9j

Scribd : https://tinyurl.com/ycwatdxu

DOI : https://doi.org/10.38124/ijisrt/26feb569

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The objective of this paper is to emphatically proclaim that knowledge is not infallible, and that all paradigms and models in science are subject to constant revision and modification, subject of course to rock solid data and reliable evidence. The main vehicle upon which this paper rides is of course the inductive approach to research which me must support and fight for tooth and nail, subject of course, to cost and time concerns and considerations. We begin this paper by furnishing quotes of eminent scientists and thinkers in support of our stance, and then review the concept of data analysis, along with the different types of data. The core concepts and postulates of our paper are then presented along with what we call the “primary axis”, “secondary axis”, and pattern identification. Multi-dimensional data bucketing is almost always required, and these need to be vetted and validated against real-world data. The latter needs to be a continuous process, and theories and frameworks need to be revalidated constantly and continuously. This paradigm evolved in the context of geographical analysis such as the claimed out of Africa dispersal of humans, and the origin of language, but this can be used for temporal analysis and other forms of analysis as well. Therefore, some other examples are also provided. We do therefore, hope, expect and anticipate that this paper will become an important one in the twenty-first century philosophy of science.

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