Archive for the ‘References’ Category

Statistics is dead, long live Statistics!

It was March 7th this year, when this mail from the ASA found its way to the ASA members: On first sight, it didn’t look like that one needs to pay too much attention, but in the longer pdf-version, you can read these six principles: P-values can indicate how incompatible the data are with a […]

Significantly insignificant

I usually enjoy reading the articles in the significance magazine published by the RSS. Not only is it a glossy magazine (quite uncommon for statistics as a discipline) but also does it often feature very nice case studies from real life problems that matter. Not so for the article in the current issue (December 2015) […]

Is there something like “old” and “new” statistics?

The blog post by Vincent Granville “Data science without statistics is possible, even desirable” starts talking about “old statistics” and “new statistics”, which started some more discussion about how statistics and data science relate. Whereas I agree that there is something like “old” and “new” thinking of the role and the tools in statistics, I am less […]

As Good as it Gets

Developing software in academia usually does not lead to commercial products, and if the intention is just this, the academic qualities often fail to reach common standards. Nonetheless, there is always the hope that commercial products might pick up the ideas generated in academic software projects. Being involved in many software projects on (interactive) statistical […]

Happy Statistics

Some weeks ago I was browsing through the categories of TED Talks and was surprised to find an entry on statistics – which is regraded to be a very boring topic by most people. The talk I chose to watch was by Nic Marks – as I tried to avoid yet another Hans Rosling talk. […]

On Twisters and Killer Tornados

Given the trouble I got into after my post on the Japan earthquake, I probably should stay put when it comes to looking at data on hazardous events … More seriously, as statistician (or data analyst in general) we often lack the expertise from the domain expert, who usually collected the data. Today, in a […]

Statistical Graphics vs. InfoVis

The current issue of the Statistical Computing and Graphics Newsletter features two invited articles, which both look at the “graphical display of quantitative data” – one from the perspective of statistical graphics, and one from the perspective of information visualization. Robert Kosara writes from an InfoVis view:  Visualization: It’s More than Pictures! Information visualization is […]

A Design Classic demystified?

Mr. Beck’s London Tube map is a real design classic. Besides the timeless and universal design, the chosen geographical distortion has always been a point of discussion. At fourthway [via infosthetics] we find a nice animation between the “real map”, which is geographically correct and the stylized map, which is optimized for reading and aesthetics. Here […]

The Good & the Bad [3/2011]

This post could as well be called “Which Smartphone is right for you?”, or “Plotting conditional distribution – but the right way!”. Here is the original visualization from Nielsen, which is not really bad, but still hides the important message to some extent. Kaiser adequately pointed out that some features – important features – of […]

Too Hot to handle?

This is the ideal post to combine Infographics/Visualizations with the user interface aspect. I found it on Kaiser’s Junk Charts. Having spent only a few years of my life in the US and being inculturated in orderly and standardized Germany, I can tell that most faucets here come pretty close to the “should be” situation. This […]

Sharpen your Eyes

We definitely live in a world of overflowing information – certainly more than a human can and wants to digest. Of course, the internet is the principal motor for this, but it also happens with the design of simple everyday’s things. Antrepo has a nice example of how product designs can be reduced to what […]

Pretty Pictures [vs|and|or] Hard Models

It is a common theme when statisticians look at data visualization output – they ask for the model. Although I am usually not an unconditional friend of building models (especially before you understand the data), but I feel the need for some kind of model in order to make this visualization more than just a […]