# Statistics are basically about finding out what is random and what is not

Last updated: Sep 2024 | Estimated read time: 5 min

## TLDR

In our daily lives, we spend a lot of time discussing patterns and randomness. Even if you're not interested in statistics or epistemology, you probably do too. For example, when you say "there are more yellow cars in this town", you're talking about a pattern. It's a very simple one, but it's a pattern.

The point is that we are always trying to determine what's random and what's not. When someone says that there are more yellow cars, this person is actually saying that he or she has observed so many yellow cars that it's unlikely that it's just a coincidence: it's a pattern. Statistics is just the formal way of doing that.

## A word on randomness

The question of "Is our world fundamentally random or not?" is a very complex one and lots of smarter people than me have tried to answer it. However, for this post, it's not an issue. It doesn't matter if the world is really random or not, because at the human scale, it's like it is: the number of yellow cars that you'll see in a city might not be random, but for you it is, and it will behave like a random phenomenon.

To the contrary, we can define pattern as something that is not random. In an intuitive way, we can think of a pattern as "when X happens, Y tends to happen too, but not always".

## How it works

A big part of how people use statistics is to calculate the probability of some events to happen, supposing that there is no pattern. For example, if men and women have the same grades in a class, the difference in terms of average grades between them should not be higher than a certain value. So we compute this theoretical value and compare it to the real one. If the real difference is higher than the theoretical one, we can say that there is a pattern: men or women have higher or lower grades. With this example, we can say that the grades are not random, but they are at least partially determined by the gender.

This is a very simple example, but it's the same for more complex ones. The point is that we are always trying to determine what's random and what's not. And statistics is just the formal way to do that.

## Ok but i can do it myself

Yes, if you're the God of probability, you can do it yourself. But for the rest of us, we need statistics. We tend to overestimate our ability to find pattern. We specially tend to see patterns where there are none.

Imagine, if Monday morning you see someone with a red bike at a coffee place. Then, on Tuesday morning, you see the same person with the same red bike at the same coffee place. Same thing on Wednesday. Who would not think that this person will be there on Thursday? Not many people. You starting to understand an important problem: we need to define a method to determine if something is a pattern or not. And statistics is one way of doing that.

## Statistics and epistemology

For the red bike example, statistics will likely tell us that we can't say that it's a pattern. Actually, we can't say much about it. And that's the whole thing: it's frustrating and counter intuitive. For some reason, we tend to love finding patterns and hate not even being able to say there aren't any.

When actually thinking about how much we should believe something, based on our experience, it's generally disappointing. There are so many biases, not enough real data and a lack of definitions. One good thing is that it does not really matter in our everyday life: very few people want to know if there are more yellow cars in this city. But for science and important decision making, it's a big deal.

A practical application of this is the marketing. Companies now hire data analysts to examine their data and trying to draw conclusions on what customers are thinking / want / need, and not just some random opinions. It's a way better method of understanding customer behavior rather than theorethical knowledge or thinking that we know what they want. If it's done the right way, we can actually gain a lot of insights. And even if the decisions made did not lead to the expected results, we litterally can't have any regrets: and that's the beauty of it. We were wrong for the right reasons.

## Closing remarks

The whole point of statistics, and even machine learning, is to determine what's random and what's not. Patterns are what make the world a bit predictable, and that gives us a lot of power. Thanks to these tools, we can anticipate diseases in early stages, detect frauds, understand inequalities, and so much more.

It's a very powerful tool, but it's also a very complex one, especially because technical knowledge is not enough. We need to understand the epistemological part of it, and that's a whole other story. Doing statistics is actually fighing against our own brain, and epistemology can help us to be more convinced (and win the fight!).

## Feedback

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