Descriptive and inferential statistics are two fundamental branches of statistics that serve different purposes in analyzing and interpreting biological data, particularly in the field of biochemistry. Descriptive statistics focuses on summarizing and organizing observed data to provide a clear overview of its main features, without making any predictions or generalizations beyond the sample. In contrast, inferential statistics involves drawing conclusions, making predictions, or testing hypotheses about a larger population based on sample data. The distinction lies primarily in their objectives: descriptive statistics describes what the data shows, whereas inferential statistics interprets and extends the findings to make broader conclusions. In biochemistry, descriptive statistics are frequently used to summarize experimental measurements such as enzyme activity, protein concentrations, or metabolite levels. Common descriptive measures include central tendency metrics like the mean, median, and mode, which indicate the typical value within a dataset. For instance, if a biochemist measures the glucose concentration in the blood