Redhyte: a self-diagnosing, self-correcting, and helpful hypothesis analysis platform. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Toh, Wei Zhong; Choi, Kwok Pui; Wong, Limsoon
• Is Part Of: Journal of information and telecommunication. Volume 1:Issue 3(2017)
• Page Start: 241
• Page End: 258
• Publication Date: 2017-07-03
• Subjects: Statistical hypothesis testing -- hypothesis analysis -- hypothesis mining -- data mining; Telecommunication -- Periodicals; Information technology -- Periodicals; 621.382
• DOI: 10.1080/24751839.2017.1347419
• Languages: English
• ISSNs: 2475-1839

Abstract

ABSTRACT: An interactive platform for 'Rapid exploration of data and hypothesis testing', named Redhyte, is described in this article. Redhyte provides a more efficient and encompassing hypothesis testing procedure than the conventional statistical hypothesis testing framework, by integrating the latter with data-mining techniques. Redhyte is self-diagnosing (it tries to detect whether the user is doing a valid statistical test), self-correcting (it tries to propose and make corrections to the user's statistical test), and helpful (it searches for promising or interesting hypotheses related to the initial user-specified hypothesis). Hypothesis mining in Redhyte consists of the following steps: context mining, mined-hypothesis formulation, mined-hypothesis scoring on interestingness, and statistical adjustments. And Redhyte supports multiple hypothesis-mining metrics (e.g. several forms of difference lift) that are useful for capturing and evaluating specific aspects of interestingness (e.g. changes in trends and manner of shrinkage). Redhyte is implemented as an R shiny web application and can be found online athttps://tohweizhong.shinyapps.io/redhyte, and the source codes can be found athttps://github.com/tohweizhong/redhyte .