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Prism can identify and remove outliers when fitting a model with nonlinear regression. But what happens when you ask Prism to compare two models?

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We compared the two methods for data with no outliers, with one outlier and with two outliers. •All simulations assumed a Gaussian distribution with a mean ...

https://www.graphpad.com/guides/prism/latest/statistics/

Surprisingly, in rare cases, Prism can report zero outliers even when an oultier is present. This page explains why.

GraphPad FAQs

... outlier(s), are sampled from a Gaussian distribution ... Three of those data sets seem to include an outlier, and indeed Grubbs' outlier test identified outliers ...

https://www.graphpad.com/guides/prism/latest/statistics/

The apparent outliers are gone. Grubb's test finds no ouliters. The extreme points only appeared to be outliers because extreme large values are common in a ...

GraphPad FAQs

The other possibility is that the outlier was due to a mistake - bad pipetting, voltage spike, holes in filters, etc. Since including an erroneous value in your ...

GraphPad FAQs

But Grubbs' test doesn't find any outliers in the data set on the right. The presence of the second outlier prevents the outlier test from finding the first one ...

https://www.graphpad.com/guides/prism/latest/statistics/

When analyzing data, you'll sometimes find that one value is far from the others. Such a value is called an outlier, a term that is usually not defined ...

https://www.graphpad.com/guides/prism/latest/statistics/

The presence of the second outlier prevents the outlier test from finding the first one. This is called masking.

GraphPad FAQs

... outlier. Then after removing those outliers, it it performs a standard least-squares fit on the remaining points. How robust regression works. Based on a ...

https://www.graphpad.com/guides/prism/latest/curve-fitting/