Statistical inference is the branch of statistics that deals with drawing conclusions about a population based on information collected from a sample. In practical research, it is often impossible or too expensive to study every member of a population. Therefore, researchers select a representative sample and use statistical methods to make reliable inferences about the entire population. Statistical inference provides a scientific basis for decision-making by measuring the uncertainty associated with conclusions. It forms the foundation of data analysis in fields such as economics, business, medicine, education, engineering, and social sciences. The main objective of statistical inference is to estimate unknown population parameters and to test assumptions or claims about the population. Since conclusions are drawn from sample data, there is always some degree of sampling error. Statistical inference uses probability theory to quantify this uncertainty and assess the reliability of the conclusions. Two major components of statistical inference are estimation and hypothesis testing. Estimation is the process of using sample data to determine the value of an unknown population parameter. Population parameters include measures such as the population mean, population proportion, population variance, and population standard deviation. Since the exact