Alright. So now let's discuss some of the problems we might run into when interpreting graphs. Let's look at this left graph first. We've got wages and education, so education is on our x-axis and wages on our y-axis, and you might expect to see something like this where as education goes up, so do our wages, right? That's probably why a lot of you are studying right now, and the idea is that yes, your wages will go up in the future as you are more educated. Cool, but what are we missing here, right? There's another factor to the compensation equation that we might be leaving out. So the idea here is that sometimes a graph might omit a variable. So we call this the omitted variable bias, alright? This omitted variable, and the idea here is while education is important for determining your wage, so is your experience, right? So experience in this case is going to be our omitted variable, right? I would imagine that there is some correlation between the amount of experience you have and what your wage is going to be. Alright, so that is one way that a graph can omit some information, right? We're omitting a variable here; it's not showing us the full picture.
I'm going to get out of the picture now, to use this right graph to explain what we call reverse causality. Reverse causality. So remember, causation is where one thing comes before the other, right? It's a cause-and-effect relationship. So reverse causality, you can imagine, is where you take the effect and you think that the effect causes the cause, right? You're looking at it backwards, not the cause causing the effect, where you're looking at the effect causing the cause, so it's reverse causality. The idea here is something like this where we have police officers on the x-axis and crime on the y-axis, and the idea here is that it's saying that as police officers increase in a city, so does the crime. Right? And that seems kind of backwards. Right? So the idea is like you look at a city with a lot of crime and you're like, hey, there are a lot of police officers in that city. So since there are a lot of police officers, that must be why there's a lot of crime. Instead of thinking of it the other way around, right? So a city with a lot of crime has a lot of police officers. So they're kind of mixing up the variables here. The idea being that the graph is showing that police officers cause crime rather than crime causing police officers.
Cool. So those are our 2 types of pitfalls that we might run into: an omitted variable and reverse causality. Cool? So let's move on to the next video.