Measurement error and misclassification of variables is frequently encountered in epidemiology and involve variables of considerable importance in public health such as dietary intakes, physical activity, smoking, and environmental pollutants. Further, the rising interest in research with electronic health records has brought new challenges and renewed interest in robust and practical methods to address error prone exposures and outcomes. If not handled properly, analyses of error-prone data can lead to biases associations of interest. This course discusses the issues raised by measurement error and practical approaches for analysis that mitigate its effects. Our aim is that participants gain the knowledge and confidence to understand the effects of measurement error and to apply techniques for measurement error correction in their own work. The emphasis will be on practical application and worked examples will be used throughout. Examples will be given using the R software. The course will begin with a discussion of the effects of measurement error in regression analyses. Focus will then move to techniques for mitigating those effects via statistical analysis and study design. Several methods will be introduced, including regression calibration, simulation extrapolation (SIMEX), likelihood-based methods, and Bayesian methods. The primary focus will be on measurement error in explanatory covariates, but error in response variables will also be discussed. Issues arising from different types of error and study design will also be covered. The session will draw on the work of the STRengthening Analytical Thinking for Observational (STRATOS) Initiative’s measurement error topic group, which is led by Professor Laurence Freedman and Dr Victor Kipnis.