Abstract
Bias, confounding, and random variation/chance are the reasons for a non-causal association between an exposure and outcome. This chapter will define and discuss these concepts so that they may be appropriately considered whenever one is interpreting the data from a study. Several types of common bias will be discussed (e.g. measurement bias, sampling bias, etc.) and effect modification (interaction) will be explained.
You’re like the Tower of Pisa-always leaning in one direction [1]
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Glasser, S.P. (2014). Bias, Confounding, and Effect Modification (Interaction). In: Glasser, S. (eds) Essentials of Clinical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05470-4_17
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DOI: https://doi.org/10.1007/978-3-319-05470-4_17
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