A COMPARATIVE STUDY OF TRIMMING METHODS FOR CENTRAL TENDENCY MEASURES
Abstract
Recent advancements in statistical methodologies have significantly improved the ability to detect treatment effects and test the equality of central tendency parameters while simultaneously controlling Type I error rates and enhancing statistical power. This paper reviews the progress made in these methods, particularly in one-way independent group designs. Key developments include more flexible statistical techniques and computational advancements, which address practical challenges that were previously considered insurmountable. The integration of robust theoretical frameworks with advanced computational tools has enabled researchers to better manage Type I errors and optimize the detection of genuine treatment effects. These advancements are crucial for applied researchers, as they enhance the capacity to identify true differences between groups and increase the likelihood of detecting meaningful effects. This review provides insights into the evolution of these methods and their implications for improving research outcomes.
Keywords:
Treatment Effects, Central Tendency, Type I Error, Statistical Power, One-Way Independent GroupsDownloads
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https://doi.org/10.5281/zenodo.12923380Issue
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Copyright (c) 2024 Muhammad Faisal Bin Ismail
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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