percentiler

Percentiler is a term used to describe software tools, libraries, or conceptual approaches that compute percentiles from numerical data. It is not tied to a single standardized product, but rather to implementations that estimate percentile values for a dataset or sample.

A percentile is a value such that a given percentage of observations fall at or below it. Different definitions exist for how to handle the space between data points, and percentiler implementations typically offer multiple methods, including nearest-rank and interpolation methods. The choice of method can affect results, especially for small samples or datasets with many ties.

Typical features of percentiler tools include accepting a dataset (array, list, or column), handling missing values, choosing a percentile p between 0 and 100, and returning the corresponding data value. Some tools also output the full percentile distribution, confidence intervals, or a set of selected percentiles. They may support various data types, data sources, and integration with statistical or data-analysis workflows.

Common methods underpinning percentiler calculations include linear interpolation between order statistics and predefined quantile types, such as those described in Hyndman and Fan. The method choice influences the interpretation of results and is especially relevant for small samples or skewed distributions.

Applications of percentilers span statistics, data analysis, educational testing, performance benchmarking, and data science. Percentilers help summarize distributions, set cutoffs, establish norms, and compare individuals or groups by relative standing. Limitations include sensitivity to distribution shape and the chosen calculation method; percentiles are estimates and should be interpreted in context with clear documentation of the method used.