This can happen all the time in data science. Just add a few layers of bureaucracy and department separations between data gathering, cleaning/filtering, and an

This can happen all the time in data science. Just add a few layers of bureaucracy and department separations between data gathering, cleaning/filtering, and analysis and it's easy to make the wrong conclusions on observed correlations without carefully thinking through all the filtering steps that might induce Berkson's paradox.