 Once you have your group set up, you can actually carry out your experiment or your experience, however you would like to say that. And now you're going to collect data. Data collection is prescriptive. It's steady. You have to be perfect. You can't have a day where your scale is off and weighs everybody five pounds too much in all your groups. You have to be very careful to collect good data. And the other thing you have to be careful of is you have to make sure that you are collecting observations. Observations which are, I don't know, objective. You can't argue about them. You put somebody on a scale that's accurate and you're going to get their weight. And that's an objective observation. That is an example of data. As opposed to, let's throw in a big red x-arama. Inferences, these are observations that you might, these are conclusions actually that you might draw based on your observations. So you might say, oh, the protein diet is totally working based on your observations. You have to be careful that you're not including inferences or conclusions in your data. I bring this up because it's actually something that students get confused and do. So you might be thinking, dude, why would I ever do that? I don't know, but students often do. Once you have your data, you need to do an analysis.