Why Do Deletion Methods For Missing Data Reduce Statistical Power? - The Friendly Statistician

Why Do Deletion Methods For Missing Data Reduce Statistical Power? Have you ever wondered how missing data can impact the results of your analysis? In this informative video, we'll explain why methods that delete missing data points can reduce the effectiveness of statistical analysis. We'll start by discussing what deletion methods like listwise and pairwise deletion are, and why they are commonly used due to their simplicity. We'll then explore how removing data points can decrease the size of your sample, making it harder to detect true effects and increasing the likelihood of bias. You'll learn how deleting data might lead to less precise estimates, wider confidence intervals, and potentially misleading conclusions, especially when the missing data are related to the data itself. We’ll also cover why assuming data are completely random when deleting can be problematic, and how this can distort the relationships within your dataset. Finally, we'll introduce alternative techniques like imputation that help retain more data and preserve statistical power, making your analysis more reliable. Whether you're working on research projects or data-driven decision-making, understanding these concepts helps you make better choices when handling missing data. Join us for this valuable discussion, and subscribe to our channel for more insights on data analysis and statistics.

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