In an attempt to demonstrate just how obvious and simplistic that statistical fallacies can be, let's start off with the classic which everyone should already know: cherry picking. The failure to do so will be catastrophic in terms of both data outcomes and a data scientist's credibility. Here are five statistical fallacies - traps - which data scientists should be aware of and definitely avoid. The end results is the same, however: plain ol' wrong. That's not hard and fast, however, as there is definitely overlap between these 2 phenomena. Out of interest, when misuse of statistics is not intentional, the process bears a resemblance to cognitive biases, which Wikipedia defines as "tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment." The former builds incorrect reasoning on top of data and its explicit and active analysis, while the latter reaches a similar outcome much more implicitly and passively. Let's have a look at a few of these more common fallacies and see how we can avoid them. The good thing is that once they are identified and studied, they can be avoided. Given that people have been making these mistakes for so long, many statistical fallacies have been identified and can be explained. There are infinite ways to incorrectly reason from data, some of which are much more obvious than others.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |