Reservoir Sampling is a statistical method used for sampling a fixed number of items from a possibly very large or unknown-size data stream. It allows for efficient random sampling without requiring storage of all data, making it particularly useful in scenarios where data cannot fit into memory. This post discusses the basic concepts of Reservoir Sampling, its applications, and some advanced algorithms that further optimize the process by calculating how many records to skip instead of processing each record sequentially. The comments reflect enthusiasm for the clarity of the post and share personal anecdotes related to practical applications of sampling in wildlife management. Some users point to the historical significance of the algorithm discovered by Vitter, drawing connections to broader themes in telemetry and data science.