Peran Ukuran Pemusatan dan Penyebaran Data dalam Analisis Statistika
DOI:
https://doi.org/10.23969/jp.v10i04.38955Keywords:
central tendency, data dispersion, descriptive statistics, data variability, statistical analysisAbstract
Measures of central tendency and measures of data dispersion are essential components of descriptive statistics that serve to summarize, comprehend, and interpret the characteristics of a dataset. Central tendency indicators including the mean, median, and mode illustrate the representative or central value of the data, whereas dispersion indicators such as the range, variance, standard deviation, and interquartile range (IQR) describe the extent to which observations deviate from that central point. This article discusses the definitions of both types of statistical measures, their significance in data analysis, and their application across fields such as education, healthcare, and data science. Findings from the literature reveal that relying solely on central tendency without considering dispersion may lead to biased or misleading interpretations. Therefore, reporting both measures concurrently is regarded as the most effective practice to ensure accurate interpretation and enhance the quality of data-driven decision-making. The reviewed studies also emphasize that central tendency and dispersion act as preliminary indicators for identifying suitable statistical techniques in subsequent analyses, including inferential testing, regression, and predictive modeling. Thus, comprehensive understanding of these measures is crucial for researchers to analyze data objectively, precisely, and in a scientifically accountable manner.
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