Cohen’s d (wiki) is a statistic used to indicate the standardised difference between two means. Resarchers often use it to compare the averages between groups, for instance to determine that there are higher outcomes values in a experimental group than in a control group.
Researchers often use general guidelines to determine the size of an effect. Looking at Cohen’s d, psychologists often consider effects to be small when Cohen’s d is between 0.2 or 0.3, medium effects (whatever that may mean) are assumed for values around 0.5, and values of Cohen’s d larger than 0.8 would depict large effects (e.g., University of Bath).
So, you’ve probably never heard of variable fonts.
Well, I sure had not when I first came across the concept a week or so ago. And I was shocked. This looked so cool. As I adjusted the size of my browser, the text and images adjusted itself along. As I made my Chrome window bigger, the text enlarged to keep filling the space it was allowed. Insane!
Typearture is Arthur Reinders Folmer’s adventure in type, creating type designs with a focus on conceptual, illustrative and ornamental typefaces.
The typefaces in the Typearture library are not just collections of glyphs, but typefaces that use the conventions of type design and written language to tell their stories. These stories are woven throughout the typefaces, connecting A to Z and the Lemniscate to Question mark. Each character has it’s place and meaning, making each keystroke a small tale in itself.
MIT researchers have spent years developing the new drag-and-drop analytics tools they call Northstar.
Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze, and evaluate machine learning (ML) pipelines.
Northstar starts as a blank, white interface. Users upload datasets into the system, which appear in a “datasets” box on the left. Any data labels will automatically populate a separate “attributes” box below. There’s also an “operators” box that contains various algorithms, as well as the new AutoML tool. All data are stored and analyzed in the cloud.
You can read more about the tool’s functionalities in this MIT news article, which includes several promising GIFs:
Moreover, on the Northstar website you can find this longer video explaining the tool in detail.
While Northstar looks insanely cool and promising, I do worry about putting such power in the hands of people who may not have much experience with statistics and/or machine learning. We all know how easily errors and bias may slip into data-driven processes, so I am curious to see how these next-gen kind of tools will be deployed and used.
In this beautiful, online, interactive course, Noam allows you to program several GAMs yourself (in R) and to progressively learn about the different functions and features. I am currently halfway through, but already very much enjoy it.
If you’re already familiar with linear models and want to learn something new, I strongly recommend this course!
Zeit — the German newspaper — analyzed recent election results in over 80,000 regions of Europe. They discovered many patterns – from the radical left to the extremist right. Moreover, they allow you to find patterns yourself, among others in your own region.
The map is beautifully color-coded for the dominant political view (Conservative, Green, Liberal, Socialist, Far left, or Far right) per region. Moreover, you can select these views and look for regions where they received respectively many votes. Like in the below, where I opted for the Liberal view, which finds strongest support in regions of the Netherlands, France, Czechia, Romania, Denmark, Estonia, and Finland.
For instance, the region of Tilburg in the Netherlands — where I live — voted mostly Liberal, as depicted by the yellow Netherlands. In contrast, in the German border regions conservative and socialist parties received most votes, whereas in the Belgian border regions uncategorizable parties received most votes.
Zeit discovered some cool patterns themselves as well, as discussed in the original article. These include:
Right-Wing Populists in Poland
North-South divides in Italy and Spain
Considerable support for regional parties in Catalonia, Belgium, Scotland and Italy
Dominant Green and Liberal views in the Netherlands, France, and Germany
Have a look yourself, it’s a great example of open access data-driven journalism!