Neutrosophic Statistics (Smarandache in Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics, InfoLearnQuest, 156 p., 2007; Smarandache in Introduction to Neutrosophic Measure, Neutrosophic Integral, and Neutrosophic Probability, Sitech, Craiova, Romania, 140 p., 2013; Smarandache in Introduction to Neutrosophic Statistics, Sitech, Craiova, Romania, 124 p., 2014) is a branch of statistics that extends classical and fuzzy statistics by incorporating the concept of neutrosophy (Smarandache in Multiple Valued Logic Int. J. 8(3):297–384, 2002; Smarandache in Neutrosophic Theory and its Applications: Collected Papers, EuropaNova, Brussels, 2014) which provides a mathematical framework for handling indeterminacy by considering three components for any proposition: truth, indeterminacy, and falsity. Classical Statistics handles only determinate data and inference methods, while Neutrosophic Statistics addresses indeterminate data, which may be unclear, vague, partially unknown, contradictory, or incomplete. It also deals with indeterminate inference methods that include degrees of uncertainty, allowing for inexact or ambiguous values and arguments in probability distributions, charts, diagrams, algorithms, and functions. Consequently, in Neutrosophic Statistics, data points are characterized not only by their probability of being true or false but also by an indeterminate component. that our improved procedure can successfully compare the efficiency of all types of uncertain number components.