Percentile calculations in SQL determine the value below which a given percentage of observations in a dataset fall. For example, the 90th percentile represents the value such that 90% of the data points are below it. This is a powerful tool for understanding the distribution of data and identifying key thresholds. For instance, in sales data, the 95th percentile of order values might indicate a high-value customer segment. Percentile calculations are crucial for understanding the spread and distribution of data, which is vital in many analytical tasks. They are particularly useful when dealing with skewed distributions where the mean or median might not accurately represent the typical value. SQL offers various ways to calculate percentiles, often using window functions, which allow you to perform calculations across a set of rows related to a given row.