Bubble artifacts
These tweets and other artifacts from my sociology/statistics bubble caught my eyes recently. I’ll try to publish similar curations of my bubble regularly from now on.
Fooled by visualization
Kieran Healy and John Mullahy show data is interpreted differently depending on how one chooses the scales of their axes:
They certainly would. See e.g. this pair (after an example by Bill Cleveland) where the perception that two lines are converging is driven entirely by the aspect ratio. https://t.co/e7huA05rGX https://t.co/gW2W38NLV5 pic.twitter.com/2CnRaQasss
— Kieran Healy (@kjhealy) April 19, 2020
Prediction is hard
And of course it’s not just difficult to not get fooled from choices of vizualisation, but it’s also not easy at all to predict s-shaped curves, as Constanze Crozier shows.
Merkel explains R values
Also related: Merkel explains the R-value and its consequence for society. It’s rare to see politicians explain network analytical measures from epidemiology so fluently. With English subtitles:
Active learning for systematic reviews
Rens van de Schoot’s and Daniel Oberski’s team at the Methodology & Statistics Department of Utrecht University develop ASReview a wonderful tool which uses active learning to make systematic reviews easier. They even created a Covid-19 plugin. Cool stuff!
