Joseph Cimpian and colleagues published a paper titled “Mitigating invalid and mischievous survey responses: A registered report examining risk disparities between heterosexual and lesbian, gay, bisexual, or questioning youth” in Child Development. This paper uses a machine-learning matching-based approach to reduce the impact of invalid data in estimates of LGBQ and heterosexual youth risk disparity estimates. The paper is not only pre-registered but is a Registered Report, which is a type of publication type to promote open science and discourage practices like p-hacking. The code, data, and pre-registration materials are all freely accessible online. The research was covered in The Conversation.