Data analysis is a critical stage of research because it enables the organization, summarization and interpretation of collected information. Categorical data consist of observations classified into distinct groups, such as gender, occupation or responses to yes/no questions. Frequency and percentage are widely used descriptive statistics for summarizing such data. Frequency indicates the number of observations within a category, while percentage expresses the frequency relative to the total number of valid observations. This paper examines the reasons for using frequency and percentage to analyze categorical data, focusing on their simplicity, clarity, appropriateness, comparability and usefulness in presenting research findings. Their application to nominal, ordinal, binary and Likert-type data is discussed, alongside their limitations. The paper argues that frequency and percentage provide an appropriate foundation for describing categorical data but should not be regarded as sufficient for every research question.
NNACHI SAMUEL KALU (2026) studied this question.