When analyzing a dataset sometimes we are working with data that does not represent the reality but it’s the result of a seleciton. This means that the seleciton made before the analysis is already creating a data bias. For example if we work with panel data we must make sure […]
In one of my previus posts about cognitive biases I mentioned the Occam’s razor principle. When it comes to data insights this is sometimes a very useful principle to keep in mind because data does’nt give insights itself but we are always asked to give our interpretation to the data, […]
“if you torture the data long enough, it will confess to anything”. Ronald H. Coase, British Economist When working with a big dataset it’s easy to fall into the confirmation bias. We are looking for some insights, and usually what happens is that even before looking at the data we […]
So after reading the Bananas Diet post we are now familiar with the causality bias concept. Another example of causailty bias i came across a few years ago for the first time is the black car crashes one. Articles like this one show that based on data anyone can conduct […]
What does Bananas and Black cars have in common? why correlation does not imply causality?
In my previous post about data visualization I have focused on different ways to present a dataset, however a very important aspect that we always underestimate and do not consider as fundamental is the data preparation one. It is widely known that a data scientist spends most of his working […]
A few months ago (i just realized while typing it’s almost already a year ago) i took an R course to learn how it can help with data visualization, and i have to admit it has been quite useful even for my day to day where i am not specifically […]