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.
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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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 Cottrell and you're listening to Developer Tea. And my goal on the show is to help 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 the 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 in whatever form you want. You can 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. Now, 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 biased 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 baseness. 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, 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. 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. 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 were to actually hold a contest and maybe 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? Uh. Maybe you don't take into account the current state of the economy, for example. 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. You know. Being deeply in debt. So it's important to understand bias. Not because. You know. In order to live and survive. You must be totally unbiased. And a 100. 100% perfectly rational person. Uh. That's essentially impossible to do. And. Uh. It is economically prohibitive. Right? There's no way that you're going to be able to. Absolutely. Rationally. Uh. 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. And this can be. This can have kind of the opposite effect. Right? We may stay in a job. Far too long. Based on fear. Uh. We may believe that. Oh. I'm slightly less. Employable. Uh. 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. Uh. Uh. Magnitude wise. Matching up. To reality. And 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. And we should have. 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. Uh. A gradiated decision. Right. Uh. From. A one to ten score. For example. How employable. Do you believe. That you are. And then ask yourself. Why. 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 Kahneman's. Thinking fast. And slow. And 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. 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 in bets. 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. You know. 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. I encourage you. To subscribe. And whatever. Podcasting app. You're using. To listen. To this show. Right now. Thank you so much. For listening. And until. Next time. Enjoy your tea.