Guide to Essential BioStatistics IX: Critically evaluating experimental data - the Q-test for the identification of outliers

In this ninth article in the LabCoat Guide to BioStatistics series, intended as a basic refresher for scientists and technicians, we learn about the Q-test for the identification of outliers. To experimentally test a hypothesis we use significance levels, power, and effect to design an experiment. The experimental parameters are defined under the assumption that our data will be derived from an approximately normal data set. With the results of our evaluation in hand, the next step is to critically evaluate the experimental data by identifying and possibly rejecting outliers. #statistics #biostatistics #experimentalresearch #cropprotection #bioscience #GuideToEssentialBiostatistics #Experimentaldesign
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