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).
Speaking for myself and not for anyone else who has posted here.
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.
Taking post #62 at face value and following it one step further.
The bit of community side-effect dataset that nobody enjoys is that the answer changes depending on what you are trying to decide with it. Say what the decision is and the thread will converge.
Post #60 and I disagree about the size of the effect, not about the direction.
The most useful dataset this community could hold is boring: lot, supplier, service, method, date, result. That is enough to answer most of the questions people ask badly.
Independent test results are the most valuable data this community collects, and they are only comparable when the method is captured alongside the number.
Noting that the question and the thing people usually mean by it are different.
Reframing community side-effect dataset slightly, because I think the disagreement is about the question rather than the answer. If the question is "does it happen", yes. If it is "how often", nobody here knows.
Publishing the raw records alongside the summary is what makes a dataset checkable. A summary alone asks for trust that nobody has earned.
One of those cases where knowing the mechanism does not help the decision.
Fair, and the limits you put on it are the part I will remember.
A codebook describing each field takes twenty minutes and is what makes the file usable by anyone but you. Most shared datasets here do not have one.
I would treat that as a working assumption and revisit it.
Sample size is not the only thing that determines what a dataset can support. Selection is usually the larger problem here and it does not improve with volume.
The variance between people here is larger than the effect being discussed.
My experience of community side-effect dataset contradicts the reply above. I am posting it as a data point rather than as a refutation, because one person's experience is exactly that.
Building on post #71 rather than restating it.
A dataset is only useful if the collection method is described alongside it. Numbers without a protocol are a list rather than data.
Aggregating first-hand accounts does not produce evidence of the kind a trial produces. It produces a description of who chose to post, which is a real thing and a different thing.
One more caveat and then I will stop qualifying: the sample selected itself.
Taking community side-effect dataset seriously for a moment rather than deflecting: the honest position is that the community has observations and no controlled comparison, and those two things support very different sentences.
Where I part company with post #75, and it is a narrow parting.
Self-reported, unblinded, self-selected data has known biases and is still worth collecting, provided every one of those words appears in the description.
Post #75 is the version of this I will quote in future. One addition.
Date every record. A dataset assembled over two years without dates cannot distinguish a change over time from a change in who was contributing.
Post #79 answers the question as asked. The question underneath it is different.
Worth separating community side-effect dataset as a question about the compound from community side-effect dataset as a question about the documentation. They get answered by different people and only one of them is answerable here.
Version the file rather than editing in place. A dataset that changes silently under an analysis makes the analysis unreproducible.
Not a strong opinion, just a consistent one.
Answering the question post #78 raises rather than the one it answers.
Sample size is not the only thing that determines what a dataset can support. Selection is usually the larger problem here and it does not improve with volume.
That is the shape of it. The detail is where I would expect to be corrected.
Practical note on community side-effect dataset: write down what you expect before you look. The number of times I have found what I went looking for is higher than chance would allow.
Adding the measurement that post #84 says would settle it.
Self-reported, unblinded, self-selected data has known biases and is still worth collecting, provided every one of those words appears in the description.
If anyone can point at the primary source I would be grateful.
Post #82 describes the usual case. This is about the unusual one.
Date every record. A dataset assembled over two years without dates cannot distinguish a change over time from a change in who was contributing.
Publishing the raw records alongside the summary is what makes a dataset checkable. A summary alone asks for trust that nobody has earned.
I would call that likely rather than established.
Picking up post #87: that is the part I would want checked first.
The question underneath community side-effect dataset is usually "how would I tell?" rather than "what is true?", and that one has a method attached to it.
Write down what you would expect to see under each hypothesis before you collect anything. If they predict the same observation, collecting it will not help.
I would put moderate confidence on the mainstream reading of community side-effect dataset and no more. That is not scepticism for its own sake; it is where the sourcing actually stops.