Missing data is a common issue in research where some of the data points are not available or incomplete. This can occur for various reasons such as participant non-response, equipment malfunction, or human error. Researchers must address missing data to ensure the accuracy and validity of their findings. There are several methods and techniques available to handle missing data, including imputation, deletion, and model-based approaches. Researchers must carefully consider the implications of missing data on their results and choose the most appropriate method for handling it in their analysis.