Author(s): Mustapha RAKRAK

Abstract: This article explores the concept of central tendency in statistical analysis, which involves summarizing datasets through key measures: the mean, median, and mode. These measures offer distinct methods for representing the "center" of data, but their utility depends on the nature of the data distribution. The mean, while commonly used, can be heavily influenced by outliers and skewed distributions. The median, offering robustness against extreme values, is particularly useful for skewed data, while the mode provides insights into the most frequent values, often in categorical datasets. The article further examines how measures of central tendency interact with distribution shapes such as normal, skewed, and kurtotic distributions. Understanding these interactions is critical in selecting the appropriate measure for accurate data interpretation. Finally, the article highlights the importance of considering both skewness and kurtosis, which provide additional context about the data’s shape, helping guide statistical decision-making.

DOI: 10.22271/27891607.2025.v5.i1b.253

Pages: 71-75 | Views: 83 | Downloads: 37

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