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Descriptive statistics

This lecture introduces descriptive statistics as essential tools for summarizing and presenting research data clearly. It distinguishes between parameters (population measures, denoted by Greek letters) and statistics (sample measures, denoted by lowercase letters). Key descriptive methods include tabulation (frequency and cross-tabulation tables), calculations of central tendency (mean, median, mode) and variability (range, standard deviation), and visual tools like histograms, bar charts, and scatterplots. The material also covers proportions, ratios, and rates, with cautions about interpreting percentages from small samples. An example dataset illustrates practical application. Overall, descriptive statistics enable researchers to organize, summarize, and visually represent data to facilitate analysis and interpretation before further inferential analysis.