Filter-based sampling

Filter-based sampling is a method used to collect a representative subset of data from a larger dataset by selecting specific portions using predefined criteria. Think of it like using a sieve or filter to separate desired information from the rest, based on certain attributes such as age, location, or keywords. This approach helps researchers focus on relevant data for analysis, saving time and resources, while still maintaining the integrity and diversity of the original dataset. It's a targeted way to efficiently extract meaningful samples without processing every piece of data.