 Now we're going to move into latent variables. So anything that's not directly observed. Sometimes that's just errors. Sometimes that's variables are measured with error, whether that error is biased, meaning you've got an instrument that's always a little bit high or it's actually got the random error, as assumed, generally with measurement error. And you want to account for that explicitly. If you have missing data, and you want to estimate data that you didn't actually collect, and more often we hear about latent variables with proxy measures. So you've got something that you measure, and you've got something that you actually want to interpret. And I think we heard a lot of people in the project descriptions talk about, I've got these data, and this is what I want to do with these data. And generally, it's not I want to summarize those data exactly, because I think they're the true measurement of the population. You've got data that represents some bigger population. And sometimes they're actually proxies for something that you can't count. But you could also, I think the book talks about GPP being a proxy of component things that you go out and measure, and then you kind of put stuff together. And NEP would be a clear example. You put stuff together and try to make some summaries about net ecosystem productivity. So ignoring the fact that there are latent variables can have a whole bunch of outcomes, which is modeling a derived response or a flawed observation can lead to incorrect or falsely overconfident conclusions. I think everybody knows that. But if you go back and look through a lot of analyses, it does not stop most people from doing it. So we'll talk more explicitly about what is a latent variable and how might you treat it.