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The Statistics and SEO Library: the Books I Recommend (and Why)

There is a question that comes back, reliably, every time I publish an article along this path: “so, which book should I read to study these things?”. Until now I have answered one piece at a time, in the “Further Reading” section that closes each article. Here I do the reverse: I gather the whole library on a single page, with the reason each title earned its place on the shelf.

This is not a ranking and not a catalogue: these are the books I actually use, the ones many of the examples and explanations in the articles come from. Few of them, chosen with a simple criterion: each book must let anyone working with data in SEO and marketing take one concrete step forward, without requiring a degree in mathematics.

Simpson’s Paradox in SEO: When Aggregate Data Can Lie

It’s the last day of the month. We’re putting together the SEO report for our main client. We open Google Search Console, set the month-over-month comparison, and a chill runs down our spine: the site’s overall organic CTR has collapsed from 4.5% to 3.5%.

Before writing the bad-news email and bracing ourselves to justify the drop, let’s do the right thing: disaggregate the data to understand where we’re losing ground. We look at performance by device and discover something seemingly impossible:

CTR on Desktop rose from 5.0% to 5.5%.
CTR on Mobile rose from 2.0% to 2.5%.

We stare at the screen. How is it mathematically possible that performance improved everywhere, yet the overall total dropped by a full percentage point?

Sampling and Sample Size: How Much Data Do You Really Need?

The Monte Carlo Method Explained Simply with Real-World Applications

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?

A/B Test Sample Size Calculator

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.

Continue reading “A/B Test Sample Size Calculator”

Category: statistics