In PRTG reports, statistics can be shown as percentiles. But I do not know what percentiles are, how PRTG calculates them, and what kind of effect they have on the data I get.

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*This article applies as of PRTG 22*

# Percentiles in reports

In reports in PRTG, you get statistically condensed information about the data that various sensors collect. In the sensor report settings, you can choose to get percentile calculation, as opposed to raw data or normal percentages for example.

Percentiles can be computed according to various mathematical methods.

## How percentiles are calculated in PRTG

- Sort the measurements: The values of a data sample have to be ordered in a row from the lowest to the highest value. You get the individual values and the number of rows N.
- Compute the row numbers of the percentile value RN using the percentile value P and the number of rows N:
`RN = 1 + ((N-1) * P)`

There are two types of RNs: FRN = floor (RN) and CRN = ceiling (RN). - Determine the result:
`If (CRN = FRN = RN) \\then (value of expression from row at RN) \\else (value of expression for row at FRN) + (RN - FRN) * (CRN row - FRN row value)`

**Note:**You can define if you want to get the*then*or the*else*result. If you choose**Continuous**in the report percentile settings, the result is always interpolated like in the*else*part. If you choose**Discrete**, the next smaller discrete value, which is the FRN, is taken as a result, according to the*then*part.

## Example

A 95th-percentile says that in 95% of all cases, the data is below a certain value, while in only 5% of all cases, the data is above a certain value.

- Step 1: Let us assume that we have the following number of rows and measured values:

Row Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|

Value | 1 | 3 | 7 | 21 | 25 | 26 | 66 | 72 |

- Step 2:
`RN = 1 + ((8-1) * 0.95) = 7,65`

with FRN = 7 and CRN = 8 - Step 3: Since CRN = FRN = RN is not true, we calculate the
*else*-part:`Percentile value = 66 + ((7,65-7)* (72-66)) = 69.9`

So 69.9 is our continuous 95th-percentile. If we want to have a discrete 95th-percentile value, it will be the FRN, which is 66.

As you can see, in a 95th-percentile calculation of our measurements, the value number 8 (72) would be discarded because it belongs to the 5% of peaks. In our case this is true for the 95th continuous percentile as well as for the 95th discrete percentile.

## Advantage and disadvantage of percentiles

The main advantage of using percentiles is that unusually high values (like whiskers in boxplots) are not included into the averaging calculations. This means that statistics include more relevant data. In the example of the 95th-percentile, 5% of the highest measured values are discarded for the statistical report.

If you prefer not to exclude extremes, because you do not want to disguise the effect they have on your monitored IT infrastructure, do not choose percentiles in the report settings.

**Note:** For more details on this percentile calculation, please see archive.org.

Created on Mar 3, 2015 4:24:30 PM by
Martina Wittmann [Paessler Support]

Last change on Jun 30, 2022 12:40:57 PM by
Brandy Greger [Paessler Support]

Should say Column Number instead of Row Number .... ?

And Percentile value = 66 + ((7,95-7)* (72-66)) = 69.9 should be Percentile value = 66 + ((7.65-7)* (72-66)) = 69.9

Hi Raymond,

Thank you, I fixed the typo. "Row Number" does not mean "table row" but refers to an ordered row of values, so it is correct here. The table in the article just shows values in a row in this format, it is not a "data table".

Regards,

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