The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. While methods exist to quantify the potential effect of a specified unmeasured confounder, these methods should be anchored and contextualized within each study. We put forward a method for merging sensitivity to unmeasured confounding analyses with the impacts of the observed covariates. We graphically display what we call the observed bias factors with the tipping point sensitivity analysis. We illustrate the method under various study designs and provide an application created to simplify the implementation of this methodology.