Measurement scales are fundamental concepts in the analysis and presentation of data because they determine how data can be classified, summarized, and statistically analyzed. Different types of scales provide different levels of information about variables. The four major types of measurement scales are nominal, ordinal, interval, and ratio scales. Each scale has unique characteristics and applications in data analysis. The nominal scale is the simplest level of measurement used for categorizing data into distinct groups or classes. It does not indicate any order, ranking, or numerical relationship among categories. The numbers or labels assigned to categories only serve as identifiers and have no mathematical meaning. Nominal data are generally qualitative in nature and are used for classification purposes. Examples include gender categories such as male and female, blood groups such as A, B, AB, and O, types of vehicles, or names of cities. In data analysis, nominal scale data are analyzed using frequency distributions, percentages, and modes because arithmetic operations such as addition and