@article {Johri2021.10.27.466171, author = {Parul Johri and Charles F. Aquadro and Mark Beaumont and Brian Charlesworth and Laurent Excoffier and Adam Eyre-Walker and Peter D. Keightley and Michael Lynch and Gil McVean and Bret A. Payseur and Susanne P. Pfeifer and Wolfgang Stephan and Jeffrey D. Jensen}, title = {Statistical inference in population genomics}, elocation-id = {2021.10.27.466171}, year = {2021}, doi = {10.1101/2021.10.27.466171}, publisher = {Cold Spring Harbor Laboratory}, abstract = {The field of population genomics has grown rapidly with the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population-genetic insights out-paced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous non-adaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model-fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2021/11/02/2021.10.27.466171}, eprint = {https://www.biorxiv.org/content/early/2021/11/02/2021.10.27.466171.full.pdf}, journal = {bioRxiv} }