So in this module we wanna talk about performance evaluation, and as most of you know from having worked in organizations there are a couple of purposes. One is rewards and punishment. Mostly firms tie rewards, whether it's a raise or a bonus or sometimes a promotion to some kind of performance evaluation. But also firms do this for feedback. They want employees to get better at their jobs. This is a means of giving that kinds of feedback. The kind of feedback is necessary to get better at your job. But this is performance evaluation, and we're gonna focus on performance evaluation as opposed to talent management. It's really tough to compare employees, if they're not in identical situations. So we'll talk elsewhere in the people analytics course about talent management and comparing employees. Right now, we really want to focus on evaluating the performance management of a given employee. For this starting place for this lecture and a little bit for life you might think about beginning by assuming all employees are equal ability. That's a rough decent starting place. For what we're gonna do here. Begin by assuming all employees are equal ability. And now, we're trying to figure out how hard are they working and how can we improve them at their job. So the fundamental challenge in performance evaluation is that performance measures are noisy. Whatever means we wanna use for evaluating how our employees performing is imperfectly related to what their actually doing with how hard they're working or how smart they're working. And that slippage is essentially noise. This is how a statistician would think about it, this is how an economist would think about it. They're noise in the performance measures. And this is gonna be a problem for us if we're trying to infer from these performance measures something about the employee. You can think about it as a normal distribution. So for any given level of effort, you might see the performance measure line up perfectly with that level of effort or you might see it come in a little high or a little low. So a range of outcomes can occur due to factors outside the employees control. So no matter how hard they work or how smart they work they have competitors, they have team members, they have bosses, they have an economy that they're interacting with. All of these things are outside the employees control. So there's noise, there's slippage between what the employee tries to do, how hard they work, how smart they work and the performance measure that results as a consequence of that. So this is our challenge, separating skill from luck. And for a person like me, this is one of the great challenges in life. You're forever trying to separate from what we see how much is luck and how much is skill? There's an economist at the University of Chicago, John Huizinga. Who said recently in an interview, this distinction between ascertaining skill and luck shows up all the time. Who do you give your money to for investing? How much do you pay a certain employee? It's everywhere. It isn't just about sports. It's about life. So that's really what we are trying to do here in this performance evaluation module. We are trying to separate skill from luck. You might think of it as signal verses noise. We wanna understand the true signal, even though we only get noisy performance measures. So in southern environments this isn't so hard. For example, in this picture I've shown two normal distributions and you can think of this as what might result if an employee gives high effort or if the employee gives low effort. And we're gonna use for our simple model here, we're gonna use effort, but that's just a simplification. You can think of this as working smart. You can think of this, you can even though we wanna avoid for the moment thinking of this as being a high talent versus low talent. So for the moment let's just think of it as high effort. Are they working hard or are they not working hard? In the world I've described in this picture, the outcome measures that result from high effort or low effort are quite different. So you see that the high effort distribution is quite a bit higher, quite a bit closer to the good outcomes then is the low effort distribution. So that when you get a performance measure that's pretty high on the good outcomes scale, you can be pretty sure that came from high efforts. So you can infer this employee did something well. This employee did something right. You can give them positive feedback. Conversely if you see something in this world that is very low on the outcome scale, close to the bad outcomes end then you can be quite confident that that came from low effort. That's because these signals are quite separate. So in this environment, it's not very hard to tell whether your employee is working hard or not. You get pretty clean signals. Now it's not perfect. If you get an average performance measure, it can be ambiguous and that's gonna be the case often. But for a broad range of outcomes, you can be pretty confident about what kind of effort the employee gave. But in other environments, it's brutal to determine whether the employee gave high effort or low effort because of how overlapping the distributions are. So for example in this situation, the good outcome measure more likely came from high effort, but there's still a very reasonable probability that it came from low effort. That maybe someone didn't put in much work, but the economy broke just right for them or their boss covered them or the team they worked with happened to be really on that month. All of those things are outside the employee's control, but can lead to a low effort performance resulting in a positive consequence. And vice versa, you might get a bad outcome and think on average that came from low effort but there's still a very reasonable chance, because of the noise in the measure that it came from a high effort. Again, it could be that they put in 80 hours that week, but their teammates let them down or their boss made a bad decision or their competitor made an unexpected move. So in these environments, it's very difficult to ascertain the level of effort from a noisy performance measure. This in essence is the challenge with performance evaluation. It's not always quite this difficult, there's not always this much overlap but there's always some level of noise and it makes it difficult to infer, it makes it difficult to give the right kind of feedback. We want to give you some help sorting this environment because all of you are being judged in this way to some extent, and all of you are judging employees in this way. So this is gonna motivate everything that comes next essentially. We're gonna give you some examples, some techniques and importantly some biases that people have whenever they try to make these inferences. So the merger plan, first is to give you an extended example and then drop in to four specific issues and unpack those. Because there are biases associated with each of those issues and that will lead to some prescriptions which we provide as we go on and then we summarize in the last stage
