https:/.../cb6d0907-59fa-4a15-96b9-ad9601830ba0-79af2bec-ccdf-4068-89a9-ad9c0067d808.mp4?invocationId=78c07fe5-a30f-ec11-a9e9-0a1a827ad0ec

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Blow and this video, I'm going to introduce our week three module on presenting data.

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So our learning outcomes for this week are for you to be able to create plots from data,

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identify the appropriate plot for type of plot for data in question.

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I want you to be able to read and interpret a plot, refine a plot more clearly, show its data,

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but then also put these plots and our other presentations in discussions of data into a well-organized notebook to present your data analysis.

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Before we dove into how to actually present data, I want to start with talking about some of the purposes of data presentation,

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because these purposes should guide your presentation design decisions.

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They should guide your evaluation of your own presentations and those of others.

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They'll also guide my evaluation of your presentation when you are submitting assignments.

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And one of the first things we need to do is guide the reader attention to important results.

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And effective presentation is not going to have every piece of thing in it.

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It's going to draw focus and attention to the important results and make it easy for

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your reader to ask the key questions around the data analysis you're presenting.

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But then it also needs to substantiate the results and conclusions.

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So when we're presenting the data, we want to guide the reader to focus on what it is that we want them to learn,

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but also in a context that gives them the information needed to assess the validity of the conclusions that we're presenting.

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And we want to do so with integrity. It's easy to make charts that highlight the result.

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You want the user to see whether or not or not that result is rigorously defensible from the data.

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And we want to avoid making those kinds of misleading data visualizations and presentations.

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So in doing this, we want to be able to think about the audience and you're gonna be presenting data to several different audiences.

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The first audience is yourself.

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When you're working with a data set, when you're trying to understand what you're learning from it, the results of your inferences,

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your presenting data to yourself, and you need the presentation to be clear so that you understand what it is that you're learning from the data.

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So you see the next question to asks. You're not misleading yourself in the data analysis process.

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But those kinds of charts don't necessarily need the same level of Polish that a chart for an external audience.

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Would your collaborators, supervisors, et cetera, need to be able to see the data, see what you're learning?

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Maybe it's in the weekly meeting you have with your research advisor.

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You're presenting them with the results that you found.

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They are people who have a lot of knowledge of the project you're working on another problem base you're working on.

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They're gonna help you guide and refine your questions.

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Again, they perhaps don't need as much Polish as a final published result, but they're not just the ones that you're creating internally for yourself.

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You're going to be presenting to expert readers.

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If you write a scientific paper, the readers are probably usually wouldn't have some level of expertize in the topic that you're talking about.

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They may know. They'll probably know the subject in general, but they may not know your specific work.

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You may be presenting this to decision makers, especially if you're doing a data science project in an industrial or corporate environment.

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You're providing data.

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That's going to inform the decisions that your boss, who may not have significant statistical expertize or data expertize that may,

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but they're going to be using those decision, those the data and the the data that you present in order to make decisions.

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And then finally, you may occasionally be be producing or presenting data for the general public at large.

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Each of these audiences is going to require different things from your data presentation.

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So you need to understand who it is that you're presenting the data to in order to make appropriate data presentation decisions.

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When we're presenting the data, here are some questions that are going to help us understand what it is that we need to guide the reader towards.

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So we need to be clear on the reader needs to come away knowing what we sought to find out.

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This might be just explicitly stating our research questions, but they need to know the purpose that the data we're presenting is supposed to serve.

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What are they supposed to learn from it? We then they didn't then need to see what we do learn.

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And then they need to see the supporting evidence, the context to trust the conclusions.

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It's not just enough to say here's the results, but in many cases you need to provide enough data,

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enough context that they not only see what you learned, but they see why you believe it is true.

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And presentation with integrity shows the reader and really makes it clear to the reader what we learned.

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The evidentiary support behind it, why it flows from the data.

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Whereas dishonest presentation manipulates them into the conclusion without having the rigorous foundation underneath it.

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So I want to show you an example of a very bad graphic that came up out of the state of Georgia earlier this year.

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They presented campaign ad hundred network television, a graph that's purporting to show Kofod cases in various high population counties over time.

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But if you look closely at the Y at the X axis of this graph and you'll see this more clearly when you go and look at the slides,

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the axis is not sorted. It starts with the twenty eighth of April and then it goes to the twenty seventh, followed by the twenty ninth.

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Then May 1st. Then April 30th. At the end we have May 2nd.

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May 7th. April 26. May 3rd. It violates the expected convention.

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And what they're what you'd need to do to show a trend over time that time goes from left to right.

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They're sorting things in. They're putting things out of order to show the trend.

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They want. In a way that's not substantiated by the data and it takes a lot of work to make a chart.

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This bat. I'm not sure how to do it in any of the statistical software I actually use, but this is a this is an egregious example,

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but it's an example of one of the things that can happen when where when we focus on.

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The effect we want to demonstrate over the evidentiary support for it.

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I want to contrast with a. I want to contrast with a chart from W edi w e, b do BWAS charts created for the nineteen hundred parece X position.

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And these were a series of charts for an exhibition to show the economic, educational,

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etc. progress of black Americans from emancipation to the turn of the century.

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And he made a series of charts showing economic situations and things.

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And here's a bar chart that clearly shows the result.

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The distribution of economic statuses for farmers after a year of farm labor.

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And it shows we have the first categories of bankrupt and in debt.

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And then we have four different or five different levels of of non-negative return up to clearing.

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Fifty dollars or more. And it shows them the bars are proportional to the length of the data.

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Very clearly highlights these things.

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It then also does a creative thing of pulls out the separate bar that it indicates is the composite of all of the non-negative bars.

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And we can see that even if you if you add all of that, they non-negative bars together.

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It's not as many farmers as as the indebt category.

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That's not a very standard thing. These charts were hand drawn.

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But it's a creative use of the visualization to highlight in a way that's supported by the data,

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the relative distributions of a different in return levels for black American farmers at the time.

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Another one, that's another one that's creative here is this spiral bar chart.

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Again, it's an unusual thing, but the lines, we can see them going progressively longer.

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And if this were just a part of normal horizontal bar chart without the spiral that the smallest line,

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the first line, 1975, would be so small you couldn't even see us.

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This gives more space. And it shows good visualization doesn't just mean following the checklist of rules.

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It means presenting the data in a way that the conclusions and the takeaways are clear and they're rigorously supported by the underlying data.

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There's no visual tricks to make things look larger or smaller than they are.

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It transparently shows the connection between the conclusion and the underlying data that support it.

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So to wrap up, the goal of good presentation is to guide the reader to what we learned and how we know it.

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Effective. Presentation is going to highlight the important things for the reader to understand without distraction or deception.

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And we're going to see throughout more of this week and throughout more the semester how practically to go about doing that.