The previous article in this series explained basic concepts related to statistical signals and statistical noise in research. This article explains statistical noise in the context of randomized controlled trials (RCTs) and observational studies and offers suggestions on how noise in such studies may be reduced so as to better detect and understand the signal. Postrandomization bias related to RCTs and confounding in observational studies are discussed. Examples are provided to facilitate understanding.
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Commonwealth Univ, Sch Dent, Richmond, VA USA
Commonwealth Univ, Sch Med, Dept Biostat, Richmond, VA USACommonwealth Univ, Sch Dent, Richmond, VA USA
Best, Al M.
Lang, Thomas A.
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Univ Chicago, Med Writing Program, Chicago, IL USACommonwealth Univ, Sch Dent, Richmond, VA USA
Lang, Thomas A.
Greenberg, Barbara L.
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New York Med Coll, Touro Coll Dent Med, Epidemiol & Biostat, Valhalla, NY USACommonwealth Univ, Sch Dent, Richmond, VA USA
Greenberg, Barbara L.
Gunsolley, John C.
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Commonwealth Univ, Sch Dent, Richmond, VA USACommonwealth Univ, Sch Dent, Richmond, VA USA
Gunsolley, John C.
Ioannidou, Effie
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UCSF Sch Dent, San Francisco, CA USACommonwealth Univ, Sch Dent, Richmond, VA USA