Whether an aggregate is worth publishing at all: a disputed topic — does this still hold?

Whether an aggregate is worth publishing at all: a disputed topic — does this still hold? I have a specific reason for asking rather than idle curiosity, and the context is below.

I would like to understand what this number means before I repeat it anywhere.

A Janoshik report on a retatrutide lot gives 96.8% purity. The supplier certificate for the same lot states 98.1%. Both documents name a reversed-phase method; neither states the same gradient.

My question is not "who is right". It is: given that those two figures were produced by different methods, what is the largest difference I should expect from method alone, and at what point does a gap stop being explainable that way?

the opening post is right about the mechanism and I think understates the practical bit.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

Limitations of datasets: all community-collected data has limitations. The population is self-selected (people in this community are not representative of all people using these compounds). Reporting bias is real (remarkable outcomes get reported; mundane outcomes do not).

Collection methods: ask how the data were collected. Longitudinal tracking over months is stronger than retrospective recall. Prospective measurement (done while experiencing something) is stronger than memory afterward.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.

Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.

Using data in discussions: datasets are useful as reference points when someone claims something unusual. "I have not seen that reported in the data" is different from "that is impossible", but data gives you something to say.

Bias toward positive outcomes: datasets collected by members are biased toward people who found the compounds useful. People who did not respond do not return. People who had bad outcomes might have left the community.

Combining data from different sources: datasets from this site are not directly comparable to published trials because the populations are different. They are worth reading separately, not merged together.

On post #8 — agreed on the reasoning, with one qualification.

How to contribute: if you have longitudinal data you want to add, the format is simple: date, measurement, context. Contact the maintainer of the specific dataset.

Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.

Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.