The Paper is a Lie: Practical Data Forensics for Scientific Data (eh23)

Recent high-profile cases of scientific fraud have demonstrated that scientists are only human too and sometimes individual scientists falsify their data. Since science is the basis of decision- and policy-making, we all should be able to scrutinise scientific studies. Here, we provide practical guidance and concrete tools for forensically examining scientific data and identifying potential cases of data manipulation. These approaches will enable and empower audience members to independently perform forensic plausibility checks on scientific data, as well as provide a starting point for their further, independent study. Limitations and ethical considerations when performing such analyses will also be discussed. Science is the basis of decision- and policy-making, and it is generally a good idea to "trust the science." Recent high-profile cases of scientific fraud, however, have demonstrated that scientists are only human too, and sometimes, for various reasons, individual scientists falsify their research. Accordingly, we all should be able to scrutinise and independently assess scientific studies, spotting potential cases of manipulation. Perhaps surprisingly, this is often significantly easier to do for the layperson than one may suspect. In this talk, we'll focus on a core aspect of checking scientific studies: the raw data, and their forensic examination. To get started, we begin with a brief introduction to the structure of a typical scientific study and the process of modern scientific publishing. Afterwards, we will take a look at a number of recent high-profile cases of scientific fraud together in a hands-on manner. We will thereby establish practical guidance and concrete statistical tools for identifying potential cases of data manipulation which may warrant further examination. These approaches will enable and empower audience members to independently perform forensic plausibility checks on scientific data, as well as provide a starting point for their further, independent study of additionally provided resources. Since no tool or technique is perfect, we will also talk about the limitations of the presented approaches, as well as ethical considerations when performing such analyses. This talk is directed at everyone with an interest in scientific research and everyone who enjoys critically assessing datasets for plausibility. Prior knowledge in statistics, data science and data visualisation are certainly advantageous, but not required, as all necessary theoretical foundations will be introduced during the talk. This work is licensed under CC BY-NC 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ about this event: https://pretalx.eh23.easterhegg.eu/eh23/talk/XLDNUG/