Missing data refers to the absence of information in a dataset where values are expected. This can occur for various reasons, such as errors during data collection, participant non-response in surveys, or equipment malfunctions. Missing data can lead to biased results and affect the reliability of analyses. To handle missing data, researchers often use techniques like imputation, where missing values are estimated based on available information, or listwise deletion, which involves removing any records with missing values. Properly addressing missing data is crucial for maintaining the integrity of statistical analyses and ensuring valid conclusions.