Blog
Aug 09

Accuracy, Precision and Bias

This is a quick blog outlining the difference between accuracy and precision and what this implies for assay development. It’s simple stuff but often confusing. In the next blog we will look at this in the context of assay statistical validation.

Suppose we have a sample of known value and a new assay to use for measuring this value. When measured several times with the new assay, the results will fall into one of these broad categories:

Not good: The average estimate result is nowhere near the true result i.e. it is biased and imprecise.

More tests? No, more tests just gives a better estimate of the biased result

Plan: You need to investigate and correct the cause of the bias, and  the variability

A curate’s egg*:  Good in parts.

The average result is precise, but not a good estimate of the true value. Precise but biased.

More tests? No help, this would just provide an even more precise estimate of the wrong result.

Plan: Investigate bias

A curate’s egg*: Good in parts. The average result is a good estimate of the true value, but individual results are variable. Accurate but not precise.

More tests? Yes! More data will improve the precision.

Plan: Do more tests and / or decrease variability.

Very good: All is well. The estimate you will get from the average of these tests is both accurate and precise.

Plan: Nothing to improve!

 

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About The Author

Ian Yellowlees has an engineering degree and experience in software engineering and is also fully medically qualified, with 20+ years experience as an NHS consultant. He developed Quantics’ unique ISO9001 and GXP quality management system and provides business management and medical support to Quantics.