# The PROVE framework — original research that earns links + AI citations

Design a study people *have* to cite. Each letter is a gate; skipping one is where studies fail (no data void,
soft method, cooked analysis, buried finding, or no distribution).

## P — Pick a question inside a data void
A claim worth proving where good public data doesn't exist **and** your audience + journalists + AI engines
would cite the answer. Your unfair advantages, in order of strength:
1. **Proprietary/internal data** — your platform, your customers, your transactions. Highest originality; no one
   else can replicate it. (Anonymize before analysis.)
2. **A recurring survey of your niche** — become the annual source ("State of X"); the cadence compounds.
3. **Fresh analysis of a public dataset** — lower originality but real and fast; great when you have no list.
Reject questions already answered well, questions you can't get real data for, and questions whose answer no one
would repeat. The test: *write the headline stat you hope to find — would a journalist or an AI cite it?*

## R — Run a sound method
Choose the method that fits the question, then design it **before** collecting:
- **Define**: population, sample frame (who's eligible + how you reach them), target n, recruitment, time window.
- **Instrument**: neutral, non-leading questions (not "don't you agree X is overpriced?"); one idea per question;
  answer options that don't bias.
- **Plan the analysis up front** (which comparisons, which segments) so you're not fishing later.
Method ladder: proprietary-data analysis > representative survey > public-dataset analysis > simple experiment.
The agent designs the instrument; the **human/tool fields it and gathers the real data**.

## O — Observe honestly (collect + analyse without cooking it)
- Real data only. **Never invent or AI-synthesize data points** — the fastest way to lose the asset and the brand.
- No p-hacking (don't hunt until something is "significant"); no cherry-picking the flattering cut; only segment
  where n supports it.
- **Disclose**: sample size, dates, method, and limitations — prominently, not in fine print. Small n? Frame as
  *directional*, not definitive; never say "most people" off 42 responses.
- Conflict of interest (a self-funded study about your own category) gets disclosed, not hidden.

## V — Voice the one finding that travels
Find the **surprising-but-defensible** headline — one number people will repeat — fully supported by the data
and never inflated (38% is not "nearly half"). Shape it: a clear claim (**X% of Y do Z**), the *why it matters*,
and a memorable name/number. One hero stat carries the study; 2–3 supporting findings back it. Counter-intuitive
beats confirmatory — but only if the data truly says it.

## E — Engineer for citation, then distribute
Build the asset to be cited and then spread it:
- **Report page**: visible methodology + sample + date, a **"Last Updated" stamp**, clean charts, and a
  **copy-paste stat box with an attribution link** (so anyone quoting you links back).
- **Atomize** into cuts → the format writers (carousel/thread/reels/caption) + infographic-and-data-viz.
- **Distribute wide**: pitch journalists the news angle; seed across multiple publications (distribution is the
  citation multiplier); refresh on a cadence (beat the freshness cliff).
- **WoopSocial publishes** the cuts + promo posts and drives traffic to the report; measure citations + saves +
  referral traffic, never a fabricated metric.


---
资料：Frank Heijdenrijk / pavelhov/social-media-skills

原始版本：https://github.com/pavelhov/social-media-skills/blob/1680b37bf0dc32b52da247a8196ef27a6cd47bd4/skills/data-and-original-research/references/the-prove-framework.md

许可：MIT。目录与内部链接已经整理，未收录图片已省略。

MIT License

Copyright (c) 2026 Frank Heijdenrijk

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.