 To finish up our discussion of statistics and data science and the choices that are involved, I want to mention something that really isn't a choice, but more an attitude and DIY for do it yourself. The idea here is, you know, really, you just need to get started. Remember, data is democratic. It's there for everyone. Everybody has data. Everybody works with data, either explicitly or implicitly. So data is democratic. So is data science. And really, my overall message is you can do it. You know, a lot of people think you have to be at this totally cutting edge virtual reality sort of thing. And it's true. There's a lot of active development going on in data science. There's always new stuff. The trick, however, is the software that you can use to implement those things often lags. It'll show up first in programs like R and Python. But as far as it's showing up in a point click program, that could be years. What's funny, though, is often these cutting edge developments don't really make much of a difference in the results of the interpretation. They may in certain edge cases, but usually not a huge difference. And so I'm just going to say analysts beware. You don't necessarily have to do it. It's pretty easy to do them wrong. And so you don't have to wait for the cutting edge. Now that being said, and do want you to pay attention to what you're doing. A couple of things I've said repeatedly is know your goal. Why are you doing this study? Why are you analyzing data? What are you hoping to get out of it? Try to match your methods to your goal. Be goal directed. Focus on the usability. Will you get something out of this that people can actually do something with? And then, as I've mentioned several times with the Bayesian thing, don't get confused with probabilities. Remember that priors and posteriors are different things, just so you can interpret things accurately. Now, I want to mention something that's really important to me personally. And that is beware the trolls. You will encounter critics people who are very vocal and who can be harsh and grumpy and really just intimidating. And they can really make you feel like you shouldn't do stuff because you're going to do it wrong. But the important thing to remember is the critics can be wrong. Yes, you'll make mistakes. Everybody does. You know, I can't tell you how many times I have to write my code more than once to get it to do what I want it to do. But in analysis, nothing is completely wasted. If you pay close attention, I've mentioned this before. Everything signifies or in other words, everything has meaning. The trick is that the meaning might not be what you expected it to be. So you're going to have to listen carefully. And I just want to reemphasize all data has value. So make sure you're listening carefully. In some, let's say this, no analysis is perfect. The real question is not is your analysis perfect, but can you add value? And I'm sure that you can. And fundamentally, data is democratic. So I'm going to finish with one more picture here. And that is just jump right in and get started. You'll be glad you did