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.
Picking up post #88: that is the part I would want checked first.
The most useful single question about a method: what would it fail to detect? Every method has an answer and few documents state it.
The general case is well covered; this is the awkward specific one.
On post #90 — agreed on the reasoning, with one qualification.
I disagree with the framing of repeatability versus intermediate precision above, and I think it is a substantive disagreement rather than a terminological one. Setting out why, so it can be checked.
The reasoning depends on an assumption that is doing a lot of work and is never stated. If the assumption holds, the conclusion follows. I do not think it holds generally.
Speaking only to repeatability versus intermediate precision as I have actually seen it, rather than as it is usually described: the effect is real, it is smaller than the thread suggests, and the variance between people is larger than the effect.
Precision has two levels worth distinguishing: repeatability within a run and intermediate precision across days, analysts and instruments. The second is where most methods lose the numbers people quote.
Following this. I have the same question and no better information than the first post.
The useful distinction on repeatability versus intermediate precision is between what was measured and what was inferred from it. Both end up in the same sentence and only one of them has error bars.
Precision and repeatability: within-run and between-run variability of the method. Acceptance criterion is typically a relative standard deviation of ≤2% for area measurements.
What would change my mind on repeatability versus intermediate precision is a second dataset collected by someone with no stake in the first. Until then I hold it loosely and I would rather say so than pretend to more.
Adding the measurement that post #98 says would settle it.
I read the earlier replies on repeatability versus intermediate precision twice before writing this, because I had assumed the opposite and wanted to be sure I was disagreeing with what was said rather than what I expected.
Limits of detection and quantitation: LOD is the lowest concentration that produces a signal above background. LOQ is the lowest concentration at which the method meets precision and accuracy acceptance criteria. Both are determined empirically.
I would rather say I do not know than round it up to an answer.
An independent laboratory's method being different from the supplier's is not a discrepancy. It becomes one only when the results differ by more than both methods' demonstrated precision.
Picking up post #102: that is the part I would want checked first.
If you are new and reading this thread for the answer to repeatability versus intermediate precision: the answer is conditional, the conditions are in the third reply, and the rest of the thread is worth skipping.
Reporting a result to more decimal places than the method's precision supports is a small dishonesty that appears everywhere. A method with a two per cent relative standard deviation does not support a figure quoted to a hundredth.
If that is already documented somewhere, ignore me and link it.
Worth stating the null on repeatability versus intermediate precision before we explain it: the observation may be nothing. That possibility deserves a sentence and usually does not get one.
Coming back to post #104, because the follow-up matters more than the original answer.
Second-hand on repeatability versus intermediate precision, so weight it accordingly — someone whose method I trust told me this and I have not verified it myself.
Precision and repeatability: within-run and between-run variability of the method. Acceptance criterion is typically a relative standard deviation of ≤2% for area measurements.
I would put a moderate confidence on that and no more.
The question underneath repeatability versus intermediate precision 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.
Reading rather than answering, but this is the post I would point somebody at.
Coming back to post #111, because the follow-up matters more than the original answer.
System suitability is the ongoing evidence that a validated method is still performing. Validation is done once; suitability is done every run, and it is the one that appears on a certificate.
Happy to be the one who is wrong here if it settles the question.
Post #111 is right about the mechanism and I think understates the practical bit.
An independent laboratory's method being different from the supplier's is not a discrepancy. It becomes one only when the results differ by more than both methods' demonstrated precision.
That is where I would start, not where I would stop.
Adding the boring version of repeatability versus intermediate precision, because the interesting version keeps getting posted and the boring one is usually right.
Check the ordinary explanations, in order, and stop when one of them accounts for what you are seeing. Most of the time the second one does.
Before the thread moves on from repeatability versus intermediate precision — what is the sample size behind the claim? I am not being difficult; I have seen the same figure quoted from an n of four and from an n of four hundred.
I read post #115 twice before replying, because I had assumed the opposite.
Where a pharmacopoeial monograph exists, a method that follows it inherits a great deal of assurance. Almost nothing discussed here has one.
The strength of my opinion here exceeds the strength of my evidence.
Adding the measurement that post #118 says would settle it.
System suitability: injections run at the start of a batch to establish that the instrument and column are performing. Acceptance criteria typically include replicate precision (RSD ≤2%), peak tailing (0.8–1.5), theoretical plates (>2000), and resolution (>1.5).
I have no interest in any supplier named above.