"The rise of populism has been substantial across the advanced world, indeed across developing countries as well. So those of us who believe in data need to be strong proponents of the publication of those data even when it produces results that make us uncomfortable.
Tag: DataScience
Michael Brand, on the Science of Data Science
"We do not teach people that making mistakes is not just right, but it's the only way of learning. It's the only way of becoming better." Honestly, I want to turn this whole episode into pull quotes! Go listen!
When simple values are complex
How do you measure temperature? It's more complicated than you expect.
Axes of awful
This is another example of how there are no absolute rules in data science (except for: there's no such thing as a perfect dataset - that one holds inviolable!). Everything is context. The y-axis not starting at 0 is sometimes ok. Pie charts are sometimes a great way to compare values. A line graph is sometimes useful for discrete data.
What is wrong with this data analysis?
So someone is using quantitative data to justify something. How can you figure out whether the analysis is valid, or whether there are holes in it you could drive a truck through? It's not always easy. Sometimes it's not even possible, without access to the raw data! But here are some starter questions you can ask about the data analysis, to help figure out where the issues are.