So the key to defeating bias is to develop a consensus. This is what we've established. And biases are going to change the way that you view statistics. So we're going to talk about how to develop that consensus, how to create that baseline measurement expectation in those methods. And we're going to talk about ways that you can ultimately begin to trust those statistics and defeat or at least work around some of your biases. My name is Jonathan Cutrell and you're listening to Developer Tea. My goal in this show is to help you, hopefully you're a driven developer, help you as a driven developer, connect to your career purpose, and do better work so you can have a positive influence on the people around you. And this episode is dedicated to doing better work. That's the side of the purpose of this podcast that we're focusing on when we talk about bias. But the other kind of side effect of this is that as you begin to work in ways that are seeking truth above ego, as you begin to work in ways that allow you to see your bias for the first time, maybe ever, and defeat it. Work beyond that ego. Work beyond that bias. You start to uncover a better sense of purpose because here's the reality. Most people are handicapping their work, they're handicapping their careers, their relationships by allowing bias and allowing their ego to get in the way. You would be much happier, we would be much more focused, much more productive, and ultimately we would work better together, collaboratively, we would build better things, we'd have a better impact on the world if we could recognize our faults first, if we recognize our ego, if we recognize our bias, and then we do something about it. So this show is about doing better work, this episode is about doing better work, but it's also in a kind of a more fundamental way, this is going to help you seek after a more purpose-driven career as well. That's the reason we talk about these kinds of things, about uncovering and fixing, or at least being aware and finding a way to work around these failures of being a human. We've already established why. Why bias is important, why it can have a negative effect on the work you do. We've established the statistics and data and all of these things that have been traditionally kind of seen as cold or calculated measures, and often are not really respected in small data scenarios, right, in a small company or at an individual level, these things are discarded quickly or otherwise misused or misunderstood. They aren't applied appropriately, they aren't usually learned from correctly, because we have this perception that data is something different from information. Data is something different from a description or a measurement, right, so we need to unravel this, first of all in ourselves. We need to develop a better understanding of how statistics matter, how we can think rationally better with statistics, and how statistics don't really have an agenda. Many people have agendas, and so when we're trying to understand how statistics apply, or if we believe that something is skewed, we need to be looking at the people as closely at least as we are looking at the data itself as we're looking at the numbers. We have learned about some methods, some ways of eliminating variability or eliminating skews in your data, right? So for example, redoing a given experiment so that you have essentially the more times that you try and experiment and get the same results, the more authority that result has, and this is called replication. There are tons of research methods and specific ways of going about research, more in the academic field that can help eliminate bias as well. Of course, things like double blind studies and having a good control set and having a significant enough sample of data in order to analyze it properly, having learned if you're doing some kind of machine learning to analyze your information, then you'll have different segments of data, for example, your training set versus your test set versus the whole doubt. And all of those things have different meanings. We aren't really going to cover all of this on the show, but suffice it to say that data and statistics and all of these things that we collect that we measure, we need to do so carefully. And as we go about collecting information in a way that we can use it rationally, as we do that, if we are practicing these more careful behaviors, especially if we know why those behaviors are in place, then we're much more likely to trust those statistics. And in order to make this practical for you as a developer, you don't have to go and read about all of the various research methods that eliminate variability or eliminate bias or skew, you don't have to know about everything. What you do need to understand is a way to measure that you and someone else can both come to the same conclusion, both come to the same measurement when looking at a given subject. Right? So you need to be able to measure something with an objective process that someone else can also measure that same thing with the same process, having not seen your measurement and arrive at roughly the same conclusion. This baseline consensus, right, this is you developing a consensus that allows you to have a reliable measurement. By doing this in this particular manner, you're going to trust the information, but beyond you, the other people you work with and collaborate with, they will trust it as well, because the method is shared, the method is agreed upon. It's in a way, it's like a protocol, right? You have a way of doing this particular measurement and it's something that everyone agrees upon. In addition to having this kind of a defined process of measurement to increase your confidence, you also need to go through the process of understanding statistics and why they matter. That's kind of what we've been doing on the last couple of episodes, try to introduce some of these pieces of information about data and about statistics and about measurement and about quantification so that people can parse them and hopefully come to better conclusions about what this stuff means to them, right? But it does help for you to develop a trust in that rationality. This may take looking through information for yourself. It may take reading case studies, reading books about data-driven decision-making, for example. These are all useful ideas. Certainly the book that we always recommend when discussing bias and rationality and statistics is thinking fast and slow by Daniel Coniman. This is kind of the introductory book. It's a dense read. At the very least go and read, maybe Derek Sever's has a summary of this book, but it is certainly worth the read. But essentially what you'll find in this book is good reason to trust rational decision-making. You'll also, and perhaps more importantly and more pronounced, you'll find that humans very often fail at making rational decisions. This is because of the thing we discussed in the last episode, the biases that we fall prey to. So we're going to talk about today's sponsor and then we're going to talk about ways that you can circumvent some of these biases. Today's episode is sponsored by Wu Commerce. You need good defaults. This is something every developer needs. It's something we've talked about on the show before. Things that you can trust rather than having to pick your tools for every single project and rather than having to go and vet the code yourself, you need tools that you can trust that have been tested by thousands of other people. These are useful defaults. And Wu Commerce is exactly that. In fact, if you've ever shopped online more than about three times, you've probably used Wu Commerce because Wu Commerce powers roughly 30% of all online stores. Wu Commerce is built on top of WordPress, which is an open platform. And this also means that it's totally hackable. 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Go and check out what Wu Commerce has to offer to you as a developer. Head over to WuCommerce.com slash Developer Teato get started today. That's WuCommerce.com slash Developer Tea. Thank you again to WuCommerce for sponsoring today's episode. By the way, just before this episode, I had some excellent T. This was a slightly sweet T. And it was made by this incredible company called Mad Monk T. Now, if you've never had loose leaf T, you've heard about it on the show, hopefully, before. This is the way that T really is meant to be consumed. It's the best brewing process for T that I've ever had. The cool thing about this is that the T itself, when you make a little pot of T with loose leaf T, you can restep it again. And so, Mad Monk T is one of my picks. In fact, they're the only pick that I have as of right now. And I encourage you to try it out if you've been interested in loose leaf T before. Head over to madmonkt.com. And if you use the code Developer Tea, you'll get 15% off your first order. So we're talking about how to defeat bias on today's episode. This is a huge topic. And it's something that researchers spend their entire careers focusing on even single biases. So we're not going to cover all of the information that you need to know in order to defeat every bias and become a mental superpower wielder. That's not going to happen. What can happen is you can start to change the way that you interface with your own thoughts. What does that mean? Well, if you've practiced meditation, for example, you know that you can observe your mind working. It's kind of a strange phenomenon, but you do not necessarily have to get caught up in the thoughts that you have or even in the beliefs that you have. You can take a step back and watch those thoughts occur. And to our current conversation, you can take a step back and you can analyze if maybe right now you are being affected by a bias. And the reality is that we're all affected by biases, no matter how aware we are of those biases. Because this is not something that is a behavioral challenge, it's not a habitual challenge. This is something that is kind of pre-wired into our brains in different biases for different people, but even the most educated and most aware individuals, even the most awarded economist, even Daniel Coneman himself who wrote the book on the subject, accepts the reality that they're going to operate under some kind of biased mentality. There's some kind of biased behavior, right? And so what we have to do is first recognize that that exists, first recognize that we're going to operate with our bias intact. And sometimes we won't even be able to see it. In fact, perhaps most often we won't even be able to see it. So it's very important that first of all we recognize so that we can accept this reality because if we accept the reality, then we can take action steps to find ways of working around it. If we don't accept it, then we're very unlikely to take any kind of action to circumvent any of our biases. So one action that you could take, for example, and this is a very common way of dealing with bias is to have other people who think differently than you to collaborate with. This is kind of a way of evening out your biases. Other people are not going to have less or necessarily more biases than you, but they are going to see your interactions differently than they see their own. They're going to see your statements, your information, your beliefs. They're going to see it differently. In fact, they're going to observe the entire world differently than you observe it. And so when you react out of bias, perhaps their bias wasn't necessarily triggered. Part of this is because bias is very deeply a part of our own experiences and often is informed by things like trying to protect our cells from loss. So if I'm acting in a biased way because I'm trying to protect myself from personal loss, then perhaps another person who doesn't have the threat of the same loss can watch that behavior and identify it. Now this requires an immense amount of trust between you and the people that you collaborate with on a day-to-day basis. It also requires transparency, and as we've already said before, identifying that your ego has no place in your work. Lastly, it's very important that as you begin to use data or measured information to make better choices, to make better decisions together in your work, or even in your personal life, as you begin to use this information to make more rational, more informed decisions, it's important to remember that your bias is going to try to change the way you view that information. You are more likely to discredit information, for example, that you didn't already agree with, and you're more likely to really latch on to information that you already did agree with. When possible, make your decisions before you know the information that will determine which route you take. In other words, create a decision tree. Sometimes it's only two branches large, but if this, then that kind of tree. If you have data that leads you in one direction, then you know you're going to take that direction. If you have data that leads you in another direction, then you know you're going to take that direction. Create this more structured way of making decisions rather than you know kind of negotiating with the data, right? This is something that happens so often with data-driven decision making is negotiating with the data and calling that analysis, trying to explain the data rather than allowing the data to explain itself. So determine before seeing the results or before seeing any kind of quantification, determine how much you trust the information that you've received, not based on the results, but rather based on the methods used. Determine if you believe that the methods used were well thought out, and determine if they are based on a consensus, determine if they are replicable, determine if your sample size was large enough. And hopefully what this will allow you to do is create a more informed way of believing before you see the results rather than judging the results of that information ahead of time. Now as researchers have found out, sometimes this even is too difficult to do because how do you evaluate just how good information is? And so once again, back in the world of academia, we found ways of deciding just how good data is. For example, correlation scores, things like p-value and those kinds of things. But for most developers and most data-driven decision making scenarios, you're not going to be dealing with that level of information. Really what you're going to be dealing with is very simple things. Like for example, a moving average. How long did it take you to complete Project X? So that when Project Y comes along and it looks a whole lot like Project X, you can use information, things that you learned in the process of completing Project X to inform how you're going to behave during Project Y. This is very important to do. This is really kind of the fundamental core tenets of learning, taking past information and using it in order to predict future. This is such a key part of your career as developer and it's how you're going to improve, become better as a human. Thank you so much for listening to today's episode. I hope you will take all of this to heart as a developer because these kinds of things are going to make or break your effectiveness. But even more than that, if you can find a way to be more truthful with yourself, with your coworkers, be more honest about what you think, and to be more truthful to your brain about how wrong it can be, you're going to see your career kind of light up. The purpose driven kind of undercurrent, that fuel that we talk about, you're going to find more of that because you're going to click into this constant improvement cycle. Thanks again for listening. Thank you again to WooCommerce for sponsoring today's episode. Head over to WooCommerce.com slash developer teed. Get started today. If you have enjoyed what you've heard on today's episode, then I can almost guarantee that you're going to enjoy future episodes because this is kind of a bread and butter talk, the types of information that we share on the show all the time. Go ahead and subscribe and whatever podcasting app you use. Now if you have found developer teed to be valuable, then I request something from you. Don't request donations, don't request any of those things, of course, going in and engaging with our sponsors is always a good thing. But beyond any of that, what I request of Developer Teal listeners is that you take just a few minutes to leave a review and a rating in iTunes. This is the best way to help other developers find developer teed. Thank you so much for listening and until next time, enjoy your tea.