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Lies, Damn Lies, and Statistics: Ethics in Quantitative Research In-Person
Over the last few years, there has been increased scrutiny on quantitative research driven by several high-profile research scandals. More researchers are recognizing and speaking out about the ways that quantitative research can be manipulated or used to mislead, including cherry-picked data, p-value hacking, salami slicing, data fabrication, misleading visuals, weak effect sizes, inaccessible data, opaque analyses, and overblown claims. These practices are often the result of a lack of information and training more than a desire to mislead. This session focuses on ways that people use quantitative data and statistics to mislead, both intentionally and unintentionally, how to recognize misleading data and claims, and how to avoid these practices in your own research.
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