This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1 · go to the accepted answer.
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).
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.
post #62 answers the question as asked. The question underneath it is different.
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.
On post #60 — agreed on the reasoning, with one qualification.
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.
Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.
Two things before anyone answers the substance.
First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.
Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.
Worth separating two things that post #64 runs together.
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.
Picking up post #66: that is the part I would want checked first.
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.
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.
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.
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.
Picking up post #71: that is the part I would want checked first.
Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.
Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.
The correction was fair and I had been repeating something I had not checked carefully enough.
Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.
I read post #75 twice before replying, because I had assumed the opposite.
Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.
This follows post #75 rather than contradicting it.
Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.
post #79 answers the question as asked. The question underneath it is different.
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.
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.
On post #78 — agreed on the reasoning, with one qualification.
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.
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.
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).
post #84 is right about the mechanism and I think understates the practical bit.
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.
Worth separating two things that post #82 runs together.
Two things before anyone answers the substance.
First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.
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.
post #88 answers the question as asked. The question underneath it is different.
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.
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).