Sparse sampling refers to the technique of collecting data points at low density or infrequent intervals, often used to reduce computational load or resource consumption in data analysis and modeling. This approach necessitates careful consideration of the trade-off between the quantity of data and the quality of insights derived, as well as the potential for missing critical information between samples.