Data generation techniques are methods used to create synthetic data for various purposes, such as testing algorithms or training machine learning models. These techniques can include random sampling, where data points are generated based on predefined distributions, and simulation, which mimics real-world processes to produce data that reflects actual scenarios. Another common technique is data augmentation, which involves modifying existing data to create new samples. This can include transformations like rotation or scaling in images, or adding noise to numerical data. These methods help improve model performance by providing diverse datasets without the need for extensive real-world data collection.