Sampling algorithms are techniques used to select a subset of data from a larger dataset. This process helps in making inferences about the entire dataset without needing to analyze every single data point. For example, in statistics, researchers might use sampling algorithms to gather opinions from a small group of people to understand the views of a larger population, like voters in an election. There are various types of sampling algorithms, such as random sampling, where each member of the population has an equal chance of being selected, and stratified sampling, which divides the population into distinct groups before sampling. These methods help ensure that the sample accurately represents the larger dataset, leading to more reliable results.