Medidas Descriptivas

Today we are literally surrounded by data, our team's results , economic reports, you name it. But how can we truly understand what they're telling us without drowning in a sea of ​​numbers? Well, for that there are some tools that are simple, but truly incredibly powerful and will help us find the answers. Sounds like an

impossible mission, doesn't it? But this is precisely the question that descriptive statistics helps us to answer. It's about nothing more and nothing less than taking a real chaos of information and turning it into something clear, concise, and above all, useful. Okay, let's get right into this. To understand any dataset, we will rely on three

fundamental pillars. You have to imagine it as if you were a police sketch artist who has to describe a crowd. First, we need to know what the average face looks like, that is, the central tendency. Then, of course, how different are the faces from each other? That's what dispersion is. And finally, where is that

one specific person within that entire crowd? In other words, the position. Okay, let 's start with the first pillar. To find that average face of our data. The question here is, what is the most representative or typical value of the entire group? Let's get to the heart of our data. The average is, without a doubt,

the most famous measure. It is simply the sum of all the values ​​we have divided by the number of values. Come on, the one we all learned in school. A practical tip. In Excel, this is calculated on the spot using the formula equals, average, and range. Very easy. And here, pay attention, because this is where

things get interesting. This slide shows us the true Achilles' heel of the stocking. Notice how a single outlier , a 90, which could be an error or a super exceptional case, drags down the average and completely distorts our picture of what is typical. The average has gone from 6.75 to 16. That's almost