Invalid conclusions from studies example

- [Instructor] Jerry was reading about a study that looked at the connection between smartphone usage and happiness based on data from approximately 5,000 randomly selected teenagers. The study found that on average the teens who spent more time on smartphones were significantly less happy than those who spent less time on smartphones. Jerry concluded that spending more time on smartphones makes teens less happy. All right this is interesting. So what I want you to do is think about whether Jerry is making a valid conclusion or not. And why or why don't you think he's making a valid conclusion? All right now let's work on this together. This is really important to understand because you will see things like this in the popular media all the time that try to establish a causality when there might not be causality. Or at least where the study might not be able to show causality. So right now Jerry is saying he's concluding that smartphone usage, smartphone usage makes teens less happy, so he's assuming there's a causal connection. Smartphone usage causes teens to be less happy. Less happy, can he actually make that conclusion from this study based on how it was designed? Well the first thing to ask ourselves is is this an experimental study that is designed to establish causality or is it an observational study where we really can just say there's an association but we really can't make a statement about causality? Well in experimental study he would have had to have a control group and then a treatment group sometimes called a experimental group. So I'll say that's control group, that's the treatment or the experimental group. And then you randomly assign folks to one of those two groups and then you would make that treatment group use a cell phone more and see if they are less happy. That's not what happened here. What happened here was an observational study. In this study we are looking at two variables. So you have your smartphone usage and then you have the teen happiness. And they took these 5,000 randomly selected teenagers and they figured out their smartphone usage and their happiness, maybe with a survey of some kind. And then you could plot those data points. You would have 5,000 data points. So this data point right over here would be a very happy teenager that doesn't use a smartphone much. This would be a not so happy teenager that uses a smartphone a lot. And so you would plot those data points and there might be a teenager who's unhappy and doesn't use a smartphone or one that is happy and that uses a smartphone a lot. But you can see there's a trend, there's an association that in general the teenagers who use the smartphones more seem to be less happy and the teenagers who use the smartphones less seem to be more happy. But it's important to realize that the causality could go the other way around. Maybe less happy teenagers use their smartphones more and maybe more happy teenagers don't find a need to use a smartphone. Or there could be some variable that's not even being observed in this study that has a causal relationship with both of these. So there could be some other variable that might cause someone to be less happy and use their smartphone more. So in an observational study, you can really just say there is an association. You wouldn't be able to say that there is causality. So Jerry is not making a valid conclusion, it's an observational study, we've only established an association not causality.