Aggregated purity results across four services, with caveats — a second dataset posts 91–120

Community-collected datasets: some members have compiled datasets from their own experience and shared them. They are self-reported, unblinded, and therefore limited as evidence. But they show real patterns that people experience.

I read post #90 twice before replying, because I had assumed the opposite.

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.

post #92 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).

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.

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.

post #96 answers the question as asked. The question underneath it is different.

Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.

On post #94 — 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.

This follows post #96 rather than contradicting it.

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.

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.

This follows post #98 rather than contradicting it.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

I read post #100 twice before replying, because I had assumed the opposite.

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).

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.

Community-collected datasets: some members have compiled datasets from their own experience and shared them. They are self-reported, unblinded, and therefore limited as evidence. But they show real patterns that people experience.