Reliability refers to how much the test, process, or instrument produces similar results under different conditions under similar conditions. Reliability is crucial for tests intended to be stable over time. Although it’s impossible to determine Reliability precisely, various techniques exist to assess it. This article focuses on methods for computing reliability in quantitative data, including ratio and interval data. The primary purpose of this paper is to discuss the idea of Reliability and present the calculation of Reliability for commonly used research instruments in simple language with examples. The article presents methods and measures of statistical Reliability. It includes Stability, internal consistency, and equivalence measurement. The authors estimated the Stability of the instrument using Karl Pearson’s Coefficient of Correlation by adopting the test and retest method. Internal consistency of the instrument was estimated by Spearman-Brown Prophecy, Kuder-Richardson 20, Kuder-Richardson 21, and Cronbach’s alpha formulas. The Cohen kappa correlation coefficient and Fleiss kappa correlation coefficient estimated the equivalence of the instrument. It is concluded that young and inexperienced researchers should know the significance of Reliability, its measures and how to ascertain it correctly. A greater understanding of score reliability will help them to avoid misunderstandings and write and discuss cautiously about Reliability estimates.