Editing Statistical inference (section)

==== Model-based analysis of randomized experiments ====
It is standard practice to refer to a statistical model, e.g., a linear or logistic models, when analyzing data from randomized experiments.<ref name="Dinov Palanimalai Khare Christou 20182">{{cite journal |last1=Dinov |first1=Ivo |last2=Palanimalai |first2=Selvam |last3=Khare |first3=Ashwini |last4=Christou |first4=Nicolas |date=2018 |title=Randomization-based statistical inference: A resampling and simulation infrastructure |journal=Teaching Statistics |volume=40 |issue=2 |pages=64–73 |doi=10.1111/test.12156 |pmc=6155997 |pmid=30270947}}</ref> However, the randomization scheme guides the choice of a statistical model. It is not possible to choose an appropriate model without knowing the randomization scheme.<ref name="Hinkelmann and Kempthorne2" /> Seriously misleading results can be obtained analyzing data from randomized experiments while ignoring the experimental protocol; common mistakes include forgetting the blocking used in an experiment and confusing repeated measurements on the same experimental unit with independent replicates of the treatment applied to different experimental units.<ref>Hinkelmann and Kempthorne (2008) Chapter 6.</ref>