# Audit a percentile before calling it your rank

Constructed teaching cases with full context. No actual people, accounts, payments, research participants or forecasts.

Preserve the measured unit, sorted sample and exact quantile or rank convention. A value percentile, fraction strictly below a target and fraction at or below it are different calculations, especially with ties. Different interpolation rules can produce different valid small-sample values; none establishes your rank among all creators or a future prediction interval.

## Choose a value threshold or a rank question

A75 th-percentile value asks for a threshold in the measured distribution. The share of observations below a target asks a different question. State whether larger values mean more displays, more response or a longer unwanted delay; a high numeric rank is not automatically better content.

Keep posts, creators, sessions and people separate. Several posts from one creator can weight that creator more heavily in a post sample. An own-history calculation cannot establish a LinkedIn-wide rank, and a missing measurement is not an extra zero to add to the sorted sample.

## Read the actual interpolation convention

NIST notes that there is no universally adopted sample-percentile interpolation. For the labeled R7 method, the one-based position is 1+p(n−1); for R6 it is p(n+1), with the stated endpoint treatment. Interpolate between the adjacent ordered values when the position is fractional.

Record the method, p, sample size and function/version used rather than assuming every spreadsheet percentile command is identical. Methods can agree at the median but differ far into a small sample. Changing the convention after seeing a favorable threshold changes the report and needs a visible note.

## Keep strict-below and at-or-below counts distinct

For the constructed values 10,20,20,20,30 and target 20, one of five values is strictly below and four of five are at or below:20% and 80%. The tie convention determines how a rank-style percentage is reported. Do not choose whichever endpoint makes the author look more exceptional.

An interpolated value quantile may not be one of the original observations. That is a defined calculation, not a newly measured post. Keep threshold direction and equality handling when selecting a top group, otherwise tied records can be excluded arbitrarily.

## Resolve three constructed percentile claims

P1 — Constructed sorted sample 0,10,20,30,100. At p=0.75, R7 uses position 4 and gives 30; R6 uses 4.5 and gives 65. Decision: report the actual method with the threshold. Outcome: two specified calculations on five values, not evidence that a tool recovered different source data.

P2 — Constructed ties 10,20,20,20,30 at target 20. Decision: retain 20% strictly below and 80% at-or-below, or a separately declared tie-aware convention. Outcome: do not call the target unambiguously top 20% or a better creator without defining the question and population.

P3 — Constructed sample mismatch: one export contains only the most recent posts, another the selected highest-response posts. Decision: resolve selection before treating different percentiles as a performance change. Outcome: sorting and precise interpolation do not repair incomparable or cherry-picked samples.

| Method | p | Position (1-based) | Value |
| --- | --- | --- | --- |
| R7 | 0.75 | 4 | 30 |
| R6 | 0.75 | 4.5 | 65 |

## Keep the summary within its measured sample

A middle 50% range or sample percentile is not a confidence interval for a population parameter or a prediction interval for the next post. Estimating those requires additional methods and assumptions. A small number of decimal places can be more honest than displaying excessive precision from a few observations.

Read the original posts near a threshold if the purpose is an editorial decision. A high impression value is exposure evidence, not proof of clarity, preference or revenue. A quantile can organize a review; it cannot replace the reader task or explain why the count occurred.

## Save the convention and complete ordered values

The worksheet records source/unit, selection, missing values, ordered sample, p, method, target/tie rule and bounded interpretation. The offline packet reproduces the two interpolation methods and tie counts using constructed inputs; it is not an importer or a population-ranking service.

Primary statistical definitions were read on 1 November 2026. All examples are invented exercises. No creator benchmark, private account observation, prediction or independent reader judgment is established.

## Primary sources

- [NIST: percentile definitions and R6/R7 interpolation](https://www.itl.nist.gov/div898/handbook/prc/section2/prc262.htm)