This follows post #28 rather than contradicting it.
Where the specificity reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
This follows post #28 rather than contradicting it.
Where the specificity reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
I read post #30 twice before replying, because I had assumed the opposite.
Linearity across the working range is a routine demonstration and it constrains how far a result can be extrapolated. A method linear from 80 to 120 per cent of nominal says nothing about a sample at ten per cent.
A guess, clearly labelled as one.
Robustness testing deliberately varies the parameters most likely to drift — organic percentage, pH, temperature, flow — and shows the result does not. It is the part of validation that predicts whether a method will transfer.
Following this. I have the same question and no better information than the first post.
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.
The strongest argument against my own position on specificity, stated as well as I can state it, since nobody else has yet.
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).
Coming back to post #39, because the follow-up matters more than the original answer.
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.
The answer changed when I changed how I was measuring, which was informative.
Bookmarking this. I will come back when I have something worth adding.
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.
Post #43 answers the question as asked. The question underneath it is different.
Having read the whole specificity 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.
Nothing to add, except that this is the answer I would give if asked.
This follows post #43 rather than contradicting it.
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.
The most useful single question about a method: what would it fail to detect? Every method has an answer and few documents state it.
Adding it in case it saves somebody the afternoon it cost me.
Robustness testing deliberately varies the parameters most likely to drift — organic percentage, pH, temperature, flow — and shows the result does not. It is the part of validation that predicts whether a method will transfer.
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