Our job as data scientists is to demand answers from the data, even if these answers are sometimes not in line with what we would like to hear. There are many ways in which our data, the models we build with it, or the laws of statistics can push us into drawing the wrong conclusions. To be successful, we need to navigate through common pitfalls like outliers, overfitting, selection biases, and more.
In this talk, we will discuss not only how data can deceive analysts (both human-driven and technical) and what those consequences can be but also how to avoid being misled in the first place. In other words, we'll be covering how to ensure that your data is actually telling you the truth, the whole truth, and nothing but the truth.