10 Best Data Visualization Projects of 2015 by Nathan Yau.
From the post:
Fine visualization work was alive and well in 2015, and I’m sure we’re in for good stuff next year too. Projects sprouted up across many topics and applications, but if I had to choose one theme for the year, it’d have to be teaching, whether it be through explaining, simulations, or depth. At times it felt like visualization creators dared readers to understand data and statistics beyond what they were used to. I liked it.
These are my picks for the best of 2015. As usual, they could easily appear in a different order on a different day, and there are projects not on the list that were also excellent (that you can easily find in the archive).
Here we go.
As great selection but I would call your attention to Nathan’s Lessons in statistical significance, uncertainty, and their role in science.
It is a review of work on p-hacking, that is the manipulation of variables to get a low enough p-value to merit publication in a journal.
A fine counter to the notion that “truth” lies in data.
Nothing of the sort is the case. Data reports results based on the analysis applied to it. Nothing more or less.
What questions we ask of data, what data we choose as containing answers to those questions, what analysis we apply, how we interpret the results of our analysis, are all wide avenues for the introduction of unmeasured bias.