# Percentiles

In this lesson, we will learn about percentiles.

We'll cover the following

## Representing data through percentiles

Another useful description of a dataset is by using percentiles.

For this we consider ordered data, meaning data that is sorted in ascending order. The $25^{th}$ percentile marks a data point in the ordered data such that $25\%$ of the data is below this data point and thus $75\%$ is above this data point. If we say that the $25^{th}$ percentile score on an exam was 85%, then $25\%$ of the candidates scored less than $85\%$ on the exam.

The percentiles of a dataset are commonly referred to as the ‘empirical percentiles’ as they are the percentiles of the dataset, not of the underlying distribution. The $50^{th}$ empirical percentile is equivalent to the median of the data. Common intervals to look at are the $50\%$ region around the median, also called the interquartile range or IQR.

IQR runs from the $25^{th}$ empirical percentile to the $75^{th}$ empirical percentile. The $95\%$ region, which runs from the $2.5^{th}$ empirical percentile to the $97.5^{th}$ empirical percentile. Percentiles of a dataset may be computed with the percentile() function in the numpy package. The first argument is the data, the second argument is a list of percentiles:

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