Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias

By R. Kaplan, David A. Chambers, Russell E. Glasgow. Clinical and Translational Science, 2014

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Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology (2019, methodology): “With regard to the method applied, it must be stated how, for example, confounding by indication, sampling bias, selection bias, ascertainment bias and overestimation of effects are handled and how the results can be interpreted in comparison with conventional statistical methods [26, 27].”
Tourism and Economic Growth: A Meta-regression Analysis (2019, methodology): “Because an effect size is subject to sampling variability (Kaplan, Chambers, and Glasgow 2014), we recommend that researchers also report confidence intervals for sample size effects (B. Thompson 2002, 2007).”
Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies (2020, background): “In parallel with this, the concept of “cost-effective sample size” has gained importance in recent years (18).”