Enter a comma-separated data set and a percentile value (0-100) to instantly find the Kth percentile using linear interpolation.
Estimates only.
A percentile tells you the value below which a given percentage of observations fall. The 75th percentile, for example, is the value below which 75% of the data sits. This calculator uses linear interpolation, the same method used by NumPy and most modern statistics packages.
For the default data set (12, 15, 18, 22, 27, 30) at the 75th percentile: rank = 0.75 * 5 = 3.75; arr[3] = 22, arr[4] = 27; P_75 = 22 + 0.75 * (27 - 22) = 25.75.
The 75th percentile is the value below which 75% of the data points fall. If your score is at the 75th percentile, you scored higher than 75% of the group. It is also called the third quartile (Q3).
Sort the data from smallest to largest. Compute the rank = (k / 100) * (n - 1). Take the integer part as the lower index and the next position as the upper index. Interpolate: P_k = value[lo] + (rank - lo) * (value[hi] - value[lo]). For example, the 50th percentile of 5 sorted values uses rank = 0.5 * 4 = 2, which is exactly value[2].
A percentage is a fraction expressed out of 100 (for example, getting 80% of questions right). A percentile is a position within a distribution: the 80th percentile means you scored higher than 80% of the group, regardless of the raw score.
Any value above the 50th percentile is above average by definition, since the 50th percentile equals the median. In standardized testing, the 60th to 75th percentile range is typically called above average, while the 90th percentile and above is considered well above average or excellent.