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RegisterOutliers in Replicated Crossover Study [Outliers]
What is the point of detecting outliers in a bioequivalence study anyway? Unless they're really extreme values (possibly indicating an error) you should never remove them from your analysis. The only point of detecting outliers in replicate designs is to assess if the CV obtained is not due to an outlier instead of real variation in data. Or in a pilot study, to assess the robustness of your estimated CV, for instance.
Outliers do exist and they're expected 5% of the times :)
Outliers do exist and they're expected 5% of the times :)
Complete thread:
- Outliers in Replicated Crossover Study pash413 2016-07-11 09:04 [Outliers]
![[Mix]](https://static.bebac.at/img/mix.png "open in Mix view")
- Which agency? Helmut 2016-07-11 12:56
- Which agency? pash413 2016-07-11 17:01
- Residuals and Critical value Calculation? pash413 2016-07-14 08:00
- Which agency? pash413 2016-07-11 17:01
- Outliers in Replicated Crossover StudyDavidManteigas 2016-07-12 16:55
- Outliers in Replicated Crossover Study Ohlbe 2016-07-12 17:50
- Outliers in Replicated Crossover Study ElMaestro 2016-07-13 18:49
- I would stop Helmut 2016-07-13 19:56
- Outliers in Replicated Crossover Study pjs 2017-10-05 15:46
- Sample size for the FDA NTID BE decision d_labes 2017-10-05 17:25
- Which agency? Helmut 2016-07-11 12:56
Ing. Helmut Schütz