statistics Archives - Page 10 of 12

The Beta Distribution Explained Simply

Imagine we have 800 sessions on a landing page and 65 conversions. The raw rate is 65/800 ≈ 8.1%. It looks like a precise number, but is it really? Is it the true conversion probability of the page, or could it be different? And if someone claimed the page converts at least 10% — do the data confirm or contradict that?
Questions like this are everyday fare for anyone working with traffic and conversion data. Bayesian statistics offers an elegant and direct tool to answer them: the Beta distribution.

The Beta distribution is a continuous probability distribution defined on the interval [0, 1]. That sounds abstract, but it’s exactly what we need whenever we want to model the uncertainty around a proportion: a conversion rate, a CTR, a click percentage, a probability of success. Intuitively, Beta is a distribution over the possible probabilities of the event, and that is what makes it unique.

Multicollinearity, Heteroscedasticity, Autocorrelation: Three Difficult-Sounding Concepts Explained Simply (with an Example in R)

In the posts on multiple regression, analysis of variance and time series we came across three terms that seem made on purpose to scare the reader. Multicollinearity, heteroscedasticity, autocorrelation: if we want to convince someone that statistics is a dark and inaccessible subject, these are the magic words to say. 🙂

Surprise: behind the names there are three simple ideas, and above all three very concrete problems. To see them at work, let’s take the model that sooner or later anyone working in digital marketing builds: how many conversions does each euro spent on Google Ads and on Meta bring us? We’ll watch it fall into all three traps, one after the other, and learn how to recognise them and get out.

Analysis of Variance: ANOVA Explained Simply

You have three Google Ads campaigns. Or four landing pages. Or five versions of a newsletter. The averages seem different, but are these real differences or just statistical noise?

This is exactly the problem that Analysis of Variance (ANOVA) solves: determining whether at least one group differs genuinely from the others, without falling into the trap of running dozens of separate comparisons.

Statistical Parametric and Non-Parametric Tests

Have you ever run an A/B test and wondered whether you should use a t-test or a Wilcoxon test? The choice matters: using the wrong test can cause you to miss a significant result — or, worse, make you think a result is significant when it’s not.

Statistical tests fall into two broad families: parametric and non-parametric. The fundamental difference? Parametric tests assume the data follow a known distribution (usually the normal); non-parametric tests make no such assumption. This is not a technical detail — it determines which tests you can use, how powerful they are, and how much you can trust the results.

In this article we’ll look at the differences, when to use each, and how the choice impacts the analysis of your site’s data.

Multiple Regression Analysis, Explained Simply

Does content length help ranking? Do backlinks bring traffic? Do faster pages convert better?
Taken one at a time, these questions almost always get the same lazy answer: “yes, a bit”. The trouble is that in the real world factors never arrive one at a time. Long pages also tend to attract more links; pages with more links are often the most carefully made, and maybe the slowest too. Everything moves together, and the question that really matters is a different one: which of these factors weighs on traffic net of the others?

We saw, talking about correlation, that we can measure how much two variables move together; and with simple linear regression that we can use one to predict the other.
But one correlation at a time is blind to the tangle: it risks handing the same medal to a factor and to its companion, without being able to tell them apart. It is the doorway to a trap we already know, Simpson’s paradox: an association that flips sign or vanishes as soon as we account for a third variable. Multiple regression is the tool that tackles precisely this head-on — many causes together, each measured while holding the others fixed.