This lecture provides just a couple of ideas of how to make a good data analysis presentation. There are a couple of key components that I feel like are important to making a good presentation that involves data analysis, and so I thought I would just list a couple of them here. Just so you can kind of keep them in mind and think about them the next time you're making your next presentation. So, the first and I think the most important thing you want to have in a presentation is to state the question that you're trying to answer. I can't tell you how many times I've been in a presentation and we've been ten minutes in and I still don't exactly know what question this person is trying to answer. Okay. So, it's important, upfront, that you not assume that everyone is on the same page, that everyone has the same background, but to state the question clearly and succinctly so that everyone knows what the goal is. Okay. And once everyone knows what the goal is, we could all be kind of on the same page and oriented towards achieving that goal. Okay. So stating the question is very important from the get go in a presentation. Secondary to that, you might want to describe what type of question it is that you're trying to answer if it's not immediately obvious, okay. So remember that we have the six types of questions that you can try to ask. And so it's useful to the audience sometimes to know if you're trying to ask an inferential question versus a causal one, versus a predictive one. That can help to inform any discussion that may occur afterward in terms of improving your analysis or modifying it. Another thing that I like to do that I think is very useful is to show the data. Okay. It's often helpful or tempting to just show summaries, or you just kind of just not even data summaries, but just kind of summaries in words of what the results are. But it's actually very useful to show the data. But just as a warning though, this could be a double edged sword. Because people like to talk about data when they see it. So if you want people to have good discussion, to have informed discussion that may be useful to you, I think it's very useful to show the data. People love to talk about data, but, so even if you don't want that discussion to happen necessarily, you may be better off in the end if it does happen. And so to this extent, I find plots are better than tables, because plots show people, you know, a summary of the data, but they also show people deviations from what might be expected, and so plots are very useful for kind of producing discussion and kind of encouraging people to think about the data. When it's possible, if you're making a presentation and you're showing the data, if you're showing some summary or a statistic about the data, try to show a measure of uncertainty to go with it. And the reason why is it just provides for a richer discussion when you can incorporate the uncertainty into any predictions or any estimates that you make from the data. And so, try to have a broad array of measures of uncertainty so that people can get the full picture of what's going on in your analysis. So for example, if you have a primary model, it can be used to show results for say, a bunch of secondary models or to show confidence intervals for parameters. Lastly, I think it's important that when you present results from a data analysis, that you separate three things, they are the evidence, the interpretation and the decision. Okay? It's often easy to kind of conflate all these things into one sentence or to one statement and I think it helps people to provide a useful discussion if you can separate them out. So for example, if you're looking at, let's say, an air pollutant and some health outcome, you might find that the increase in the air pollutant results, you estimate that an increase in the air pollutant results in a 5% increase in the health outcome. So that's bad. So maybe the health outcome is mortality. So, an increase in the pollutant results in a five percent increase in mortality. So that's the evidence, that is the result of your analysis. You estimated some parameter and that was the result, and maybe there's a competence level that you can present around that too. So, the interpretation might be, okay air pollution is bad for you, okay, and then the decision might be we need to lower air pollution levels in whatever environment we're thinking about. So there's three separate components there that all could be evaluated independently. Given a set of evidence, you're interpretation might be that, oh if it's only 5% maybe air pollution's not so bad for you. Or it might be 5% and it's terrible. But your interpretation can be made separate from the evidence that's provided. And further more, what you do about it is further separate from what you think about it, how you interpret it, and what the evidence is. So, separating the evidence, the interpretation, and the decision can help people think about the different components. And can weigh the evidence, in terms of how they would react differently or how important that evidence is to whatever process they're involved in. So, I think doing that provides for a useful presentation and provides for a more important, a useful discussion about the meaning of your results and what we should do about them.
