update on the earlier thing assay under label with good purity means you got less of the right thing. that is a fill problem, not a synthesis problem
#test-results 2025-06-25
Wednesday18 messages4 participantstimes are UTC
Highlights from this day
- ghent_gradient — does a bad result on one lot condemn the whole supplier 19:07
- salt_bridge — if you are testing to settle an argument you will not settle it with n of 1, your mileage will differ 20:00
- ghent_gradient — post the lot, the purchase month, the lab, the number, and what you were expecting. that makes it useful later, thats one data point 20:22
quantity testing is usually a separate line item and it is the one worth paying for
post the lot, the purchase month, the lab, the number, and what you were expecting. that makes it useful later
log the boring results too. a channel that only records failures produces a false picture
quick one the results in this channel skew because people test when they are suspicious. that is selection bias and it is real
update from 22 months ago: retested the same supplier, result came back better, logged both, someone check my working
right so my result contradicts the supplier certificate, do i tell them
sorry to jump in 97.4 against a certificate of 98.1 is fine. the same supplier landing low four times in a row is a question
does a bad result on one lot condemn the whole supplier
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one bad test does not condemn a supplier. one good test does not clear one. both halves get ignored equally
log the lot number
underfill is more common than impurity. if you only test purity you are testing the less likely failure
my read after three years of this: purity is mostly fine across the market and fill consistency is where suppliers differ
if you are testing to settle an argument you will not settle it with n of 1, your mileage will differ
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nice result
anyone had a result they think was a lab error rather than a bad vial
my sheet has 8 lots from HJ with a spread under a point, which is tight
attach the pdf
post the lot, the purchase month, the lab, the number, and what you were expecting. that makes it useful later, thats one data point
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