I often talk with my students about bias when we are conducting a research project. Usually this discussion launches with me getting on a soap box to throw around words like old school methods and sound experimental design (also met with small eye rolls they think I don’t notice.) My extended rant will cover a lot about how to conduct risk reduction exercises, which after a fashion resemble agile development methods except in hacking our insights instead of code. Overall we’re pretty interested in knowing how to make good engineering decisions on use of our time; we want the greatest illumination for least cost on each step along the way as we converge to results.
With that in mind, a nice read about bias is The Trouble With Scientists. This reminds us that while there are the things we want to know, we need discipline to ensure we’re not just cherry picking data to support a conclusion we already want to reach; we need discipline to force ourselves to look coldly at what we don’t know. These go hand in hand.