This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
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.
Posting it because the silence on this was starting to look like agreement.
Where I part company with post #91, and it is a narrow parting.
Two questions I would want answered before drawing anything from the Aggregated purity results across four data above: how were the cases selected, and what happened to the ones that dropped out.
Aggregated purity results across four: I have looked for the primary source twice and failed twice. Either it does not exist or it is somewhere I do not know to look, and I would like to know which.
Units in the column header, always, and the same units down the whole column. Mixed units in one field is the commonest defect in shared spreadsheets here.
A guess, clearly labelled as one.
Appreciated. The plain phrasing does more work here than a longer post would.
Answering the question post #94 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.
Second-hand, so weight it accordingly.
Narrowing post #98, because the general version has more than one answer.
Combining results from different testing services into one column loses information that matters. Keep the service as a field.
Reading it again, the caveat matters more than the finding.
Everything in post #100 holds. The case it does not cover is the one I have.
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.
It is worth checking rather than assuming, which costs nothing.
The most useful reply I ever got about Aggregated purity results across four was a request to state my units. It sounds like pedantry and it has saved me twice.
Where a value is derived rather than measured, mark it. Derived columns get treated as observations the moment the file leaves your hands.
I read post #104 twice before replying, because I had assumed the opposite.
Independent test results are the most valuable data this community collects, and they are only comparable when the method is captured alongside the number.
I would hold that lightly until someone with a larger sample weighs in.
Aggregated purity results across four has a well-known answer and a correct answer, and the interesting work is establishing that they are the same. Nobody has done that here yet.
Adding what did not work for me on Aggregated purity results across four, since the failures never get written up and they are half the useful information.
Answering the question post #107 raises rather than the one it answers.
Version the file rather than editing in place. A dataset that changes silently under an analysis makes the analysis unreproducible.
This has been discussed before and I could not find the thread, so, again.
Bookmarking this. I will come back when I have something worth adding.
Reading this Aggregated purity results across four thread as someone who came in with a fixed view: the third and seventh replies moved me and the confident ones did not.
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.
Not disagreeing with anyone above, just adding the bit I keep having to look up.
Narrowing post #115, because the general version has more than one answer.
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.
It is worth stating the boring hypothesis before the interesting one.
Aggregated purity results across four is a good example of a question where the honest answer is boring and the interesting answers are unsupported. I would go with boring.
Where a dataset is used to support a claim in a maintained document, the version used should be cited. Otherwise the document and the data drift apart silently.
Two sentences on Aggregated purity results across four and then I will stop, because the rest is speculation and the thread is better without mine.
What is documented is narrow. What is inferred from it is broad. The gap between them is where every argument here lives.