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A risk ratio or difference from a meta-analysis is as many as ten steps away from the unobservable causal risk ratios and differences in target populations. The steps are like lenses, filters, or other fallible components of the epidemiologist’s “telescope” for observing populations. Each step is another domain where different biases can be caused. How biases combine across domains in the production of epidemiologic evidence can be quickly explained to nonepidemiologists by using a sequence of causal arrow diagrams with easy notation: (a) agent of interest, (b) background risk factors, (c) correlated causes, (d) diagnosis, (e) exposure measurement, (f) filing of data, (g) grouping of cohorts, (h) harvesting of cases and controls, (i) interpretations of investigators, (j) judgments of journals, and (k) knowledge of meta-analysts. For epidemiologists, this article serves as a review of ideas about confounding, information bias, and selection bias and underscores the need for routinely analyzing the sensitivity of study findings to multiple hypothesized biases.