The primary purpose of this chapter is to review some basic tools that provide a quantitative picture of data that show statistical fluctuations, whatever the origin of this apparent randomness. But I also hope that the examples we will discuss may train you in the subtle art of “digging” into data. The latter may not be so essential in time-honored fields like high energy or nuclear physics, where the experimentalist benefits from consolidated methods of data analysis and the theorist may not even need to go through experiments. But scrutinizing, perusing, dissecting data becomes crucial if one deals with uncharted or frontier subjects like the physics of complex systems or biophysics. In these fields, taking an original look to data is often the pathway to new insights, both for experimentalists and theoreticians. Thus, I will try and show you how unexpected features may arise from selecting the quantities to be plotted, choosing a suitable range, changing the axes, scaling the data, focusing on specific details. To make these ideas clearer, however, we better start delving into the matter.