Quintiles

Quintiles are statistical measures that divide a dataset into five equal parts. A quintile boundary is a value that marks the point below which 20% of observations fall. When applied to the data, the five groups are often referred to as Q1 through Q5: Q1 is the lowest 20%, Q5 the highest 20%. The four cutpoints at the 20th, 40th, 60th, and 80th percentiles separate the distribution into these five segments.

Calculation and interpretation: In a sample, quintiles are estimated from ordered data. If there are N observations, the quintile positions are typically around the 0.2N, 0.4N, 0.6N, and 0.8N ranks, with minor adjustments for discrete data or ties. Different software packages use slightly different interpolation rules, so reported quintile values may vary across methods. Quintiles are descriptive rather than inferential unless combined with sampling theory.

Applications: Quintiles are commonly used to summarize distributions of income, test scores, or other measures, allowing comparison across population subgroups. They are used in economics to describe income shares, in health research to stratify risk or outcomes, in marketing to segment customers, and in finance to analyze performance by ranking assets or portfolios.

Limitations: Quintiles depend on the sample and may be sensitive to outliers or skewed distributions. They do not convey information about the shape of the distribution within each quintile, and comparability requires consistent calculation methods.