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What Exactly Is Bias, Anyway?

Published 9/3/2018

We talk about bias on this show quite a bit but haven't talked about what biases are. In today's episode, we're going to identify different types of biases and give you some tools to respond when you notice yourself reacting to bias.

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Transcript (Generated by OpenAI Whisper)
We talk about bias on the show quite a bit, but I don't know that we've ever stopped to talk about what exactly a bias is. We're going to talk a little bit about this on today's episode and hopefully give you a little bit of kind of some tools that you can use to respond when you see yourself reacting to bias. My name is Jonathan Cutrell and you're listening to Developer Tea and my goal on the show is to develop driven developers like you connect to your career purpose. And I'm excited to talk about this, this kind of meta topic. We very often talk about psychology on this show. It's something that has been recognized by you as the audience. And I'm excited because I think these kinds of conversations are truly evergreen. These are the kinds of episodes that you can come back to in future years. You can download this podcast and whatever form you want to download it in and come back and listen to this again in the future. And unless we have some kind of unexpected breakthrough when it comes to psychology, then most of this content is going to remain true. There's always going to be some kind of thing that you're going to deal with with relation to bias. So what exactly is bias? Bias is the systematic leaning, right? The systematic leaning in one particular direction that doesn't match up with some average. What does this mean? This all sounds a little bit less about behavior and more about math. As it turns out, bias is best described by math or by numbers by statistics because bias is in and of itself a measurable reality. So if you were, for example, to believe a bias belief that you are better than average, this is called confidence bias, by the way. If you have this belief and you don't have evidence to prove it, then you have a bias in one direction. The rational belief, the unbiased belief is that you would, without any other information, that you are average, that you match up to average. Why is average considered the unbiased belief? This is an entirely different discussion about something called base rates. I encourage you to Google base rates if you're interested in this topic. But if you have a belief that doesn't have a basis, right? If you have a belief that doesn't have a basis, then that belief is being influenced by something other than evidence. You have a belief that is somehow kind of concocted in your brain. Perhaps this is something that is, you know, your brain is informing you based on other sensory input or based on some kind of survival mechanism, but not based on some kind of verifiable, measurable, external reality. Now the trouble with bias is not necessarily that you're wrong, right? You're not necessarily going to be wrong just because you're biased. It's very possible that you are indeed above average. The problem with bias is when you make decisions without supporting evidence, and perhaps even more nuanced is when you make decisions that are not in the same measure as reality. So for example, you may believe that you're above average and you may be correct, but you may believe that you are well above average. You may believe that in terms of, you know, let's say your ability to hold your breath the longest out of your group of friends, you may believe that you can hold your breath twice as long as the next competitor, as the average person in your friend group, when in reality you may only be able to hold it 10% as long. Of course this kind of situation doesn't have a huge impact on your life if you would actually hold a contest and maybe you bet a couple dollars. That's not really a big deal, right? But when you make large decisions based on more substantial biases, especially when those biases have nuanced differences in their magnitude, you can make some really bad decisions. Examples of really bad decisions based on, for example, overconfidence bias, let's say that you decide to quit your job because you believe that you're well more employable than the average person. And maybe you have some backing for this belief, but your magnitude is off. Maybe you don't take into account the current state of the economy, for example, right? Maybe you don't take into account that your location may have a big impact on your employability. And so even though your personal skill level may make you more employable than the average skill set, there may be other factors that you're not bringing into that decision making process. And the problem is if you choose to quit your job based on that biased belief and based on the magnitude of that biased belief, then you could end up being deeply in debt. So it's important to understand bias, not because in order to live and survive, you must be totally unbiased and a 100% perfectly rational person. That's essentially impossible to do and it is economically prohibitive, right? There's no way that you're going to be able to absolutely rationally make decisions because that would take too much time. It wouldn't even be worth your time to determine just how employable you are. Instead, very often what we do as humans is when we find out that we're biased, we often go to extremes to protect against our bias. This can be, this can have kind of the opposite effect, right? We may stay in a job far too long based on fear. We may believe that, oh, I'm slightly less employable. Maybe that's the reality I'm slightly less employable, but in order to protect myself, I'll never leave that particular job. So what ends up happening when we fall to our biases is we make decisions that are ultimately not magnitude wise matching up to reality. This is very difficult to perfectly match up to reality, but we should have ways of checking our biases and we should have ways of evaluating and making more rational decisions than we normally would. One simple way for you to evaluate things on this more rational basis is to ask yourself instead of creating a binary decision of, oh, I should leave my job today or I'll never leave my job, create a more gradient, a gradiated decision, right? From a one to ten score, for example, how employable do you believe that you are? And then ask yourself why? When you create these steps, these scales, you're essentially giving yourself more granular decision making tools than if you were to create this kind of black and white dualistic way of thinking about decisions. I hope this discussion on bias has been helpful and I hope it's been enlightening and I encourage you if you're interested in learning more about bias. There are quite a few resources. Of course, the golden standard that we've mentioned probably a hundred times on the show now is Daniel Connemon's thinking fast and slow. There's also an excellent, there's also a book that recently came out and disclaimer, I haven't read it, I've read some of the summaries of the book and I believe that it's going to be an excellent book on the subject of making rational decisions and it's called Thinking Inbets, it's by the poker champion Annie Duke. I encourage you to consistently kind of check in with yourself and think about ways that you may increase your rational decision making. The goal is not to become a perfectly rational person but instead to make better decisions. Thank you so much for listening to today's episode. If you have not subscribed, remember this show comes out three times a week and it's easy to get behind and encourage you to subscribe and whatever podcasting up you're using to listen to this show right now. Thank you so much for listening and until next time, enjoy your tea.