Category: statistics

What is the Monte Carlo method

The story of the Monte Carlo method begins in the most unlikely way: with a mathematician in bed playing cards. In 1946, Stanisław Ulam, a Polish mathematician recovering from surgery, found himself playing solitaire to pass the time. Being a mathematician, he wondered: what are the chances of winning a game?

The problem was theoretically solvable: just enumerate every possible combination of cards and count the favorable ones. In practice, however, the number of combinations was so enormous that an exact calculation was completely impractical. Ulam then had an insight as simple as it was powerful: instead of computing the exact probability, why not simulate hundreds of games and count how many times you win?

Continue reading “The Monte Carlo Method Explained Simply with Real-World Applications”

One of the most common questions when planning an A/B test is: how many users do I need to get a reliable result? The answer is not a magic number: it depends on the size of the effect we want to detect, the baseline conversion rate, and the level of statistical certainty we require.

Calculating the sample size in advance is essential to avoid two classic mistakes: stopping the test too early and declaring a winner that does not exist, or letting it run too long, wasting traffic and time. In other words, it is about finding the right balance between resources and rigour.

If you have read the article on A/B Testing, you will recall that power analysis is the statistical method that lets us determine this threshold. And if you have studied confidence intervals, you already know that significance level and test power are not abstract concepts but operational levers that directly affect sample size.

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Introduction

Machine Learning is changing the way we see the world around us. From weather prediction to medical diagnosis, from content recommendations on streaming platforms to financial fraud detection, Machine Learning is increasingly present in our daily lives.

But what exactly is it, and how does it work? In this post, we will explore the fundamental concepts of Machine Learning and see how it can be used to solve real-world problems. We will also look at how to get started with Machine Learning, what resources are available, and how to use this technology to improve both work and everyday life.

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The Gini coefficient is a measure of the degree of inequality in a distribution, and is commonly used to measure income distribution.

These few words alone are enough to grasp the extraordinary importance of this index for economic and political studies, and why it is worth getting to know it a little more closely.

Continue reading “The Gini Index: What It Is, Why It Matters, and How to Compute It in R”

Contingency tables are used to evaluate the interaction between two categorical variables (qualitative). They are also called two-way tables or cross-tabulations.

Searching for relationships between two categorical variables is a very common goal for researchers. Think, for example, of the classic question that marketers ask: who is more likely to buy certain product categories, young or old people, men or women…

Continue reading “Contingency Tables and Conditional Probability”