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A common practice is to visually inspect the data, and remove outliers by hand. The problem with this approach is that it is arbitrary.

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

Outliers Use with caution Automatic outlier removal is extremely useful, but can lead to invalid (and misleading) results in some situations, ...

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

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/

Comparing models only makes sense when both models 'see' exactly the same set of data. That makes it tricky to combine outlier elimination with model ...

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

Descriptive statistics, detect outlier, t test, CI of mean / difference / ratio / SD, multiple comparisons tests, linear regression. Statistical ...

https://www.graphpad.com/quickcalcs/

Grubbs' test is quite specific -- it asks if the largest deviation is a significant outlier. It isn't fair to ask about two or more outliers by recycling Grubbs ...

GraphPad FAQs

If you choose outlier detection, this uses only the information from the means (ignoring n and SD or SEM). But once any outliers are identified and removed ...

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

The presence of one or a few outliers might be causing the normality test to fail. Run an outlier test. Consider excluding the outlier(s). •If the departure ...

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

Review the principles of nonlinear regression, including comparing models, identifying outliers, global fitting, and more.

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

With two-way ANOVA, Prism offers no nonparametric alternative and does not test for normality, homogeneity of variances, or for the presence of outliers.

GraphPad FAQs