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Category: Probability

The beauty of Randomness

15/07/2025 /

In the scientific world, Randomness is a term with a slightly negative connotation. Doing things “randomly,” in everyday language, suggests improvisation, patching things together, not following a plan or logical reasoning. Imagine an analyst or scientist telling you they solved a problem using a random approach: well, you probably wouldn’t feel entirely reassured.

But in the world of statistics, things are quite different. Let’s be clear straight right away: statistics can, in a way, be defined as the science of randomness—especially its most famous branch: probability theory. However, this article isn’t about that thorny topic (raise your hand if you flinched reading “probability theory”), but rather something slightly more intriguing: machine learning, the branch of statistics that analyzes large datasets to make predictions about an uncertain outcome (the dependent variable) based on a set of predictors (also called independent variables).

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Bayes… Who? An Alternative Approach to Statistical Testing

18/04/2025 /

Imagine starting a new job and having to drive to the office on your first day. You’ve never been there before, but you know it’s next to a gym you used to go to. From experience, you remember that it typically takes about 30 minutes to get to the gym at that time of day — even though the last time you went was over a year ago (you eventually stopped going, but that’s another story).

Now it’s your first day on the job, and you need to decide what time to leave the house. Let’s imagine you don’t have access to GPS or navigation data. What would you do?

  1. Use the information you already have (even if it’s outdated) and plan for a 30-minute drive?
  2. Or ignore everything you know and randomly pick a departure time?

Naturally, the first option makes more sense — you’d rely on the information you have rather than ignore it completely.

Well, if that reasoning makes sense to you, then you’re already on your way to understanding a Bayesian approach to statistics.

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Don’t trust the latecomers

15/01/2016 /

When creating a blog, one of the first things to do is to come up with a name. Before choosing datastory.it, we considered several options, but some of them were already taken. On one of these sites, we came across a phrase that made our few remaining hairs stand on end. It went something like this:
“This site contains an algorithm capable of generating Lotto numbers that are more likely to be drawn than others.”

Such words sound to a statistician like a blasphemy sounds to a priest. Have you ever heard of “hot numbers” or “overdue numbers”? Surely you have. Well, we can guarantee you that these numbers are meaningless, and there is no algorithm capable of generating numbers more likely to be drawn than others. Let’s explore why.

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