Speaker
Description
Evaluating systematic uncertainties takes a significant amount of time and effort in many physics analyses. During their lab courses, many students learn to identify possible sources of systematic uncertainties and how to quantify them. However, they are usually not exposed to handling them using statistical methods. In this tutorial, I will offer a brief overview on parameter estimation with fits and show how this can be implemented using Python. Next, I will explain how systematic uncertainties can be incorporated to a model using nuisance parameters and other methods. Using simple examples from particle physics ,such as counting experiments and a signal plus background fit, I will illustrate how the final uncertainties on a parameter of interest change when we follow these procedures and how to estimate their effect on a measurement.
