Category: statistics

Have you ever noticed how mobile users behave differently from desktop users? It’s not just a feeling — it’s a relationship between two categorical variables. The variable “device” (mobile or desktop) and the variable “converted” (yes or no) — when we cross them in a table, we can see if there’s a systematic link.

Contingency tables (also called two-way tables or cross-tabulations) are the basic tool for this. They allow us to evaluate the interaction between two categorical variables and to quantify conditional probability — how likely an event is “given that” another has occurred.

The question about mobile vs desktop differences, for example, translates into a conditional probability: is P(conversion | device = mobile) different from P(conversion | device = desktop)? If yes, there’s a relationship. If not, the two variables are independent.

Let’s see how this works, starting with a deliberately light-hearted example.

The Poisson distribution is a discrete probability distribution that describes the number of events occurring in a fixed interval of time or area.

The Poisson distribution is useful for measuring how many events can occur within a given time horizon, such as the number of customers entering a shop in the next hour, or the number of pageviews on a website in the next minute, and so on.

Continue reading “Poisson Distribution: predicting leads, clicks and anomalies (with R and Python)”

After looking at the most famous discrete distribution, the Binomial, as well as the Poisson distribution and the Beta distribution, it is time to take a look at the geometric distribution.

Continue reading “The Geometric Distribution”

The need I feel—the fruit of many years working in this field—is to affirm the decisive importance of basic scientific rigour in analysing traffic data, so that we can calibrate our SEO interventions with accuracy, and not merely “by gut feeling” (even though feelings do matter!).

The tools available to the SEO professional are countless, and yet it is undeniable that a sense of disappointment lingers within us. Too often we deal with data of apparent strategic importance that turn out, when put to the test, to be fallacious or imprecise—mere red herrings.

Continue reading “A Brief (Personal) Manifesto for SEO”

Measures of variability are used to describe the degree of dispersion of observations around a central tendency index.

In other words, measures of variability allow us to assess how data are spread around a central value, which may be represented, for example, by the mean or the median. They provide valuable information about the distribution of data, enabling a better understanding of the phenomenon under observation.

The techniques for measuring the variability of datasets are numerous. Among them, the most widely known (and most commonly used) are:

• the range
• the mean deviation and the variance
• the standard deviation
• the coefficient of variation

We will also visualise the concepts of central tendency and dispersion by revisiting skewness and introducing the concept of kurtosis.

Continue reading “Standard Deviation and CV: when the mean alone deceives you (SEO case studies)”