Today's episode is going to be largely geared towards managers, but if you're not a manager, I encourage you to think about the things that we're talking about in this episode, both so you can share them at the appropriate time with your manager. And so you can determine, let's say you're looking for a job, maybe you can ask the folks that you are interviewing with, how they treat this particular subject. We're talking about performance reviews and specifically about results-oriented performance reviews. My name is Jonathan Cutrell, you're listening to Developer Tea. When you are looking at the performance of given individual on your team, how do you judge how well they are doing? There is no one right answer. Certainly, there are many different things that you should be looking at. And trying to prescribe all those on this episode is definitely a fool's errand. It's not something that we're going to try to do because it is also a highly contextual. It's contextual to that person, to their growth path, to your relationship with them, how long have they been on your team? What are the goals of the team? Have things been clarified and many more nuances? But when thing is almost definitely the case in every company that I've ever been at, every company that you've probably ever been at, one of the most important things you can look at is results. Or at least that's the story we are told. Are the results that this engineer is delivering good? Here's the problem with this mindset. If we only look at results, we're missing perhaps the most important part of the picture, and we're substituting perhaps the most unreliable part of the picture. Now, I don't want you to ignore the results. We'll talk about what you should be doing with results after we discuss a better framing for this. What we should be paying closer attention to, the things that we can control. We can in the long run, we can influence the results, but we cannot directly control them. And so, when we're looking at how to judge a particular person's performance, we shouldn't be looking at outcomes. Results, outcomes interchangeable for the sake of this episode. We shouldn't be looking at outcomes, specifically outcomes of their decisions. We should know what those outcomes are, but more importantly, we should be looking at the quality of their decisions. You may still be confused by this because this is a nuanced thing and we're so hard-coded to think the opposite way that the quality of our decisions is directly related to the quality of the outcome. If we do the right things and we get the right things, but you personally probably have many stories, many cases in your life that refute this. Think about the last few times that you've had failures. Do you believe that you made a bad decision? In retrospect, you might assign that label to whatever decision it is that you made. But often, there are other factors that we're not taking into consideration. Some examples might include the role of randomness or luck or external influences that we couldn't have predicted, or one that is often discounted, information that wasn't available. It was hidden. It was an unknown, unknown. We know that making decisions is not about predicting the future. We know this for ourselves, at least. We try to make the best decision that we can given the information and the skill set that we have. And so if we are judging ourselves based off of this criteria, why would we judge others based off of a results-oriented criteria? How often are you producing good results? Is only roughly correlated, not directly related, but correlated to the quality of your decisions. Truth is, if you make high-quality decisions, statistically, you should succeed more often than someone who doesn't make high-quality decisions. This doesn't mean that you're going to succeed more often than not. In other words, you may not have a 60% success rate with good decision-making. Good decisions may only yield a 10% success rate. This depends entirely on the level of influence those good decisions have on outcomes. In other words, if they can only move the needle a little bit, you could imagine, for example, that starting a company where many companies, most companies, fail, it doesn't necessarily mean that all of the companies that failed made tragically bad decisions. It may simply mean that in order to succeed, you need a fewer tries than the other people who are not making good decisions. In other words, you have two camps of people. One camp is making good decisions, reliably, the other camp is making poor decisions, fairly reliably. Interestingly enough, in both camps, you have a high failure rate, if you're talking about something like starting a business. But then you have two other very interesting categories. You have the percentage of success rate for the people who are making good decisions, which tends to be higher than the success rate of the people who are making poor decisions. But a critically important factor here is that the number of people who are making poor decisions, who also start a successful company, is non-zero. This underlines an important factor when you're talking about things like performance reviews. There are people who are making poor decisions, or maybe their skill set is inadequate, whatever those inputs that you care about measuring against, they're not very good, but they are still succeeding. They're still getting good outcomes, whether that's again, by way of luck, or maybe it's by way of opportunity or circumstance, whatever it is, it is completely possible to have one person who is making excellent decisions and has poor results, and another person who is making poor decisions and actually has good results. What this underlines, though, is that we're not interested in basing our decisions, our company decisions, our career decisions, purely on measuring and rewarding people who got lucky. This is not a successful strategy. Instead, what we care about is how many tries does it take for this person to succeed? What is their track record if we were to run, let's say, a hundred or a thousand or a hundred thousand simulations, where this person's good decisions are being played out. Fundamentally, what makes a good decision is exactly this picture. Over time, statistically, the good decisions win out. And so, unless you are incredibly unlucky, which is statistically highly unlikely, your long-term success depends less on measuring your direct results and more on measuring the quality of your decisions. If you are running performance reviews, I'm telling you that it's one of the most important things you can do to adjust the inputs for your performance reviews. So, you care not just about results. Results should be reviewed, but to wait more heavily the quality of the decision making, the quality of the inputs, the quality of the actions taken by the person before the result was known. Now, quickly, what should we do with the results information? Do we just discard it? Of course not. What we need to do is make sure that over time, our results are lining up with what we believe is kind of our canon for what a good decision is. If over time, our supposedly good decisions are not producing good results, then we may need to update what we think is a good decision. We may need to bring in more information or try to understand why we are getting consistently unlucky. It's more likely that something else is influencing in that decision-making process and we are not recognizing it. The kind of connection between the decision and the outcome, we may think they're connected more closely than they are, but there's another factor that's at play that we haven't figured out yet. So, this is why it's important to continuously review those outcomes. It's also worth mentioning that outcomes are the result of repetition mixed with good decisions. If someone is staying focused and staying directly kind of pointed towards that outcome, that is a more likely success story than somebody who makes one good decision has a failure and then quits. A successful strategy is about making good decisions repeatedly and kind of pushing through and beyond temporary failures. This is why you care about a results orientation, not because it's a direct measurement of how good the decision was, but it's a measurement of, is this person capable of following through? Thank you so much for listening to today's episode of Developer Tea. I hope you enjoyed this discussion. I hope you will change the way you think about performance, change the way you think about the quality of a decision rather than retroactively labeling it bad based on the outcome, determine whether the decision was good or bad in a decoupled way, away from the outcome, the only way that you can really measure those is over time with enough repetition to prove out how good the decision quality really is. Thanks again for listening. If you enjoyed this discussion and you want to take it a step further, you can join the Developer Tea Discord. We talk about it on virtually every episode, so you should know about it by now if you're a regular listener. Go to developertea.com slash discord to join totally free. We're not trying to sell anything there, and certainly the membership there is free developertea.com slash discord. Thanks again for listening. If you are a regular listener, then thank you so much. You probably already subscribed. If you want to become a regular listener, the best way to do that is to subscribe and whatever podcasting happy currently using. Thanks so much for listening, and until next time, enjoy your tea.