Missing data techniques are methods used to handle gaps in datasets where information is absent. Common approaches include imputation, where missing values are estimated based on available data, and deletion, which involves removing records with missing information. Each technique has its advantages and disadvantages, depending on the context and the amount of missing data. Another important technique is data augmentation, which generates synthetic data to fill in gaps. Additionally, model-based methods can be employed, using statistical models to predict missing values based on observed data patterns. Choosing the right technique is crucial for maintaining data integrity and ensuring accurate analysis.