In randomized controlled trials, prior to statistical analysis, the data are checked for outliers and erroneous data. Data-cleaning is defined as deleting-the-errors / maintaining-the-outliers. Statistical tests are, traditionally, not very good at distinguishing between errors and outliers. However, they should be able to point out main endpoint results that are closer to expectation than compatible with random sampling. E.g., a difference from control of 0.000 is hardly compatible with random sampling. As it comes to well-balanced random sampling of representative experimental data, nature will be helpful to provide researchers with results close to perfection.
However, because biological processes are full of variations, nature will never allow for 100 per cent perfection. Statistical distributions can account for this lack of perfection in experimental data sampling, and provide exact probability levels of finding results close to expectation.
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(2009). The Interpretation of The P-Values. In: Cleophas, T.J., Zwinderman, A.H., Cleophas, T.F., Cleophas, E.P. (eds) Statistics Applied to Clinical Trials. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9523-8_10
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