Aggregated purity results across four services, with caveats — a second dataset posts 61–90

I would keep Aggregated purity results across four and the decision it usually gets used for separate in this thread. They are related and they are not the same question, and merging them is why the last one went badly.

Adding the measurement that post #59 says would settle it.

The practical version of Aggregated purity results across four is three sentences long. The rigorous version is three pages and reaches the same conclusion with the conditions attached.

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.

That holds under the stated conditions and I have stated them.

A dataset is only useful if the collection method is described alongside it. Numbers without a protocol are a list rather than data.

Same conclusion as the reply above, reached differently, which is mildly reassuring.

Noted, and I have changed what I was going to do on the strength of it.

Picking up post #63: that is the part I would want checked first.

Having read the whole Aggregated purity results across four thread before replying: the question in the first post has not actually been answered yet, and three of us have answered a nearby one instead.

Where a value is derived rather than measured, mark it. Derived columns get treated as observations the moment the file leaves your hands.

If it helps: the failure mode here is usually boring rather than dramatic.

Date every record. A dataset assembled over two years without dates cannot distinguish a change over time from a change in who was contributing.

I have changed my mind on this once already, so take it as current rather than settled.

Publishing the raw records alongside the summary is what makes a dataset checkable. A summary alone asks for trust that nobody has earned.

Trying to state the Aggregated purity results across four position in a way that someone who disagrees would recognise as fair, because I do not think the version in this thread passes that test.

Narrowing post #70, because the general version has more than one answer.

Version the file rather than editing in place. A dataset that changes silently under an analysis makes the analysis unreproducible.

I would treat the number as indicative rather than as a measurement.

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.

The rule of thumb is fine; the edge cases are where it earns its keep.

The confident answers on Aggregated purity results across four and the well-sourced answers are not the same answers, which is the most useful thing I have learned reading this category.

Post #72 and I disagree about the size of the effect, not about the direction.

Where I have landed on Aggregated purity results across four, having got it wrong once in public: the direction is clear, the magnitude is not, and anyone quoting a precise magnitude has borrowed it from somewhere that did not measure it.