Thomas Maydon's Truth Seeker: How to Avoid Logical Fallacies and Cognitive Biases in Data Science
Wednesday, 28 August 2019
For some time, I have been interested in sceptical thinking and how we as humans commonly make logic flaws in our thinking. Philosophers have classified these flaws in a long list of informal logical fallacies and cognitive biases.
As an analyst and mathematician, I was interested in how these errors in thinking permeate into the world of data science. It was clear that these same logical flaws exist in analytics. So I decided to blog about it.
It all started with a blog on Post-truth (this was the OED’s word of the year in 2016). You can read that as an appendix to this eBook. This was followed by a blog series on different logical fallacies and examples from data analytics.
I completed my blog series nearly 18 months after I began it and have decided to compile it into a more easily consumable eBook.
In this book, you’ll find a host of important concept where analytics and philosophy collide. There are plenty of links to webpages with more extensive explanations where I introduce certain concepts. I hope this eBook helps you improve your metacognition (thinking about thinking) which will, in turn, help you avoid silly mistakes that are regularly made in analytics. At the very least I hope you find the eBook interesting.
Many of the examples given are related directly to credit and marketing analytics (two large areas of focus at Principa).
I hope you enjoy the book.