Aggregated purity results across four services, with caveats — a second dataset — setting out what I have, and where I think it stops being reliable.
Posting a small dataset on Aggregated purity results across four. It is mine, it is uncontrolled, and the method is stated so it can be discounted appropriately.
What I would like is not agreement but a second dataset collected by someone with no stake in mine. If one exists I would rather read it than argue for this one.
Practical experience of Aggregated purity results across four, offered as one case with the conditions stated, not as a general finding. Conditions first, because they are what make it interpretable.
Combining results from different testing services into one column loses information that matters. Keep the service as a field.
Adding this to the thread rather than to the wiki, because I am not confident enough for the wiki.
I will take the caveat as seriously as the claim, which is the point of putting it there.
Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.
The arithmetic in post #7 is right; the assumption feeding it is the part to check.
The claim about Aggregated purity results across four upthread is stronger than its source supports. I have read the source. The source says "associated with" and the post says "causes".
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.
Post #7 is right about the mechanism and I think understates the practical bit.
On Aggregated purity results across four, I would rather understate and be corrected upward than overstate and be quoted. That is a house style here and it is a good one.
Thank you for the correction. I would rather find out here than later.
I had written a reply contradicting post #10 and deleted it. Here is what survived.
Adding a small correction to the Aggregated purity results across four summary above rather than a disagreement with it. The substance holds; one of the figures is out by a factor that matters.
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.
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.
Happy to expand any of that if it is the useful part.
Publishing the raw records alongside the summary is what makes a dataset checkable. A summary alone asks for trust that nobody has earned.
This is where my knowledge stops and I would rather mark the edge than blur it.
Where I part company with post #14, and it is a narrow parting.
Version the file rather than editing in place. A dataset that changes silently under an analysis makes the analysis unreproducible.
I am describing what is, rather than arguing for what should be.
The thing about Aggregated purity results across four that took me longest to accept is that a plausible mechanism is not evidence of an effect. It is a reason to look, not a result.
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 have left out the parts I could not verify.
Reading back through, this was answered upthread and I missed it. My fault.
Everything in post #19 holds. The case it does not cover is the one I have.
The reason Aggregated purity results across four keeps being re-asked is that the answer is conditional and people quote it without the condition. It is not that the answer is unknown.
Narrowing post #19, because the general version has more than one answer.
Where a value is derived rather than measured, mark it. Derived columns get treated as observations the moment the file leaves your hands.
The general case is well covered; this is the awkward specific one.
Reading back through the Aggregated purity results across four threads from last year, the same three questions come up every time and only one of them has ever been answered properly. That seems like a documentation gap rather than a knowledge gap.
Post #23 answers the question as asked. The question underneath it is different.
Nobody has said the unglamorous part of Aggregated purity results across four yet, so: most of the variation is explained by things that are boring to write about and easy to check.
I would put moderate confidence on the mainstream reading of Aggregated purity results across four and no more. That is not scepticism for its own sake; it is where the sourcing actually stops.
Fine by me. I had wanted a stronger conclusion and there is not one available.