"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!
Tag: DataScience
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.
What’s wrong with this data?
We could easily spend years talking about all the possible problems with data, but that's not particularly helpful when you have a dataset, or someone else's analysis, in front of you here and now. So what are some simple questions you can ask about quantitative data to figure out whether you can trust it or not?