For a series of measurements $x_i = x_1, \dots, x_n$, it is useful to examine the magnitude of a certain fraction of the number of values.

NOTE: The following descriptions assume that the series of measurements is available in a sorted manner.

The p-quantile ($0 \le p \le 1$) is the value that devides the series of measurements into $i=p*n$ measurements that are smaller than $x_i$ and the rest of the measurements. It is important to note that the previously calculated value $x_i$ cannot be considered to be final. There are two rules that need to be obeyed:

  1. $i$ is a whole number: The p-quantile is calculated by interpolating $x_i$ and $x_{i+1}$: $Q(p)=\frac{(x_i+x_{i+1})}{2}$

  2. $i$ is not a whole number: The p-quantile is calculated by rounding down: $Q(p)=x_{\lfloor \irfloor)}$

The 0.5-quantile is called the median. It represents the value for which 50% of all measurements are smaller and 50% are greater or equal.

Quantiles are very useful to examine a series of measurements for outliers. Low outliers can be identified by comparing the minimum value and the 0.1-quantile. Whereas high outliers can be discovered by comparing maximum value and the 0.9 quantile.

NOTE: YYMV, so please consider using slightly higher or lower quantiles to identify outliers.

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