1. The problem discusses how the level of measurement of data affects the type of descriptive statistics used. 2. There are four main levels of measurement: nominal, ordinal, interval, and ratio. 3. Nominal data are categories without any order (e.g., colors, names). 4. Ordinal data have a meaningful order but no consistent difference between values (e.g., rankings). 5. Interval data have ordered categories with equal intervals but no true zero (e.g., temperature in Celsius). 6. Ratio data have all properties of interval data plus a true zero point (e.g., height, weight). 7. For nominal and ordinal (categorical) data, descriptive statistics focus on frequency counts, mode, and percentages. 8. For interval and ratio (numerical) data, descriptive statistics include measures of central tendency (mean, median, mode), variability (range, variance, standard deviation), and shape (skewness, kurtosis). 9. Understanding the level of measurement guides the choice of appropriate statistical methods and interpretation. 10. In summary, categorical data analysis differs fundamentally from numerical data analysis due to the nature of the data and the statistics applicable.