Welcome to our space about sensitivity analysis. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That’s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:
XX% of the estimated effect would have to be due to bias to change your inference about the effect.
But that’s just one. In this website, we discuss lots of approaches, applications, and the pluses and minuses.
Our work in the KonFound-It project is to develop—and make easy to use—sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.
Let’s get started with COVID examples. Look to the left of this page for more resources and ways to connect as well.