Hybrid approach of Hypothesis Testing to test the mean difference between two groups utilising Gaussian Distribution and Confidence Interval

Hybrid approach of Hypothesis Testing to test the mean difference between two groups utilising Gaussian Distribution and Confidence Interval | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid approach of Hypothesis Testing to test the mean difference between two groups utilising Gaussian Distribution and Confidence Interval Kazi Sakib Hasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4723876/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents an easier and new robust method for hypothesis testing to conclude significant mean differences between two independent or paired samples using the concepts of location, variability, confidence intervals and Gaussian distribution. For hypothesis testing of two samples, t-test is widely used. Beside this, Wilcoxon signed-rank test and often permutation test is also conducted. Each of these methods have their own rigorousness and drawbacks for which general people and non-statistics students often find it hard to conduct experiments using these. To fix these issues, a new method of hypothesis testing is proposed in this paper that basically utilises the properties of normally distributed data and resampling, and is relatively easier to calculate using only pen and paper. The time complexity analysis of each program is also conducted to give a concise overview about which hypothesis testing algorithm is more efficient and faster to execute, since statisticians use a lot of software nowadays for their analytical tasks. Applied Statistics Hypothesis testing Parametric method Non-parametric method Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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