Blow and this video, I'm going to introduce our week three module on presenting data.
So our learning outcomes for this week are for you to be able to create plots from data,
identify the appropriate plot for type of plot for data in question.
I want you to be able to read and interpret a plot, refine a plot more clearly, show its data,
but then also put these plots and our other presentations in discussions of data into a well-organized notebook to present your data analysis.
Before we dove into how to actually present data, I want to start with talking about some of the purposes of data presentation,
because these purposes should guide your presentation design decisions.
They should guide your evaluation of your own presentations and those of others.
They'll also guide my evaluation of your presentation when you are submitting assignments.
And one of the first things we need to do is guide the reader attention to important results.
And effective presentation is not going to have every piece of thing in it.
It's going to draw focus and attention to the important results and make it easy for
your reader to ask the key questions around the data analysis you're presenting.
But then it also needs to substantiate the results and conclusions.
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,
but also in a context that gives them the information needed to assess the validity of the conclusions that we're presenting.
And we want to do so with integrity. It's easy to make charts that highlight the result.
You want the user to see whether or not or not that result is rigorously defensible from the data.
And we want to avoid making those kinds of misleading data visualizations and presentations.
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.
The first audience is yourself.
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,
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.
So you see the next question to asks. You're not misleading yourself in the data analysis process.
But those kinds of charts don't necessarily need the same level of Polish that a chart for an external audience.
Would your collaborators, supervisors, et cetera, need to be able to see the data, see what you're learning?
Maybe it's in the weekly meeting you have with your research advisor.
You're presenting them with the results that you found.
They are people who have a lot of knowledge of the project you're working on another problem base you're working on.
They're gonna help you guide and refine your questions.
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.
You're going to be presenting to expert readers.
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.
They may know. They'll probably know the subject in general, but they may not know your specific work.
You may be presenting this to decision makers, especially if you're doing a data science project in an industrial or corporate environment.
You're providing data.
That's going to inform the decisions that your boss, who may not have significant statistical expertize or data expertize that may,
but they're going to be using those decision, those the data and the the data that you present in order to make decisions.
And then finally, you may occasionally be be producing or presenting data for the general public at large.
Each of these audiences is going to require different things from your data presentation.
So you need to understand who it is that you're presenting the data to in order to make appropriate data presentation decisions.
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.
So we need to be clear on the reader needs to come away knowing what we sought to find out.
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.
What are they supposed to learn from it? We then they didn't then need to see what we do learn.
And then they need to see the supporting evidence, the context to trust the conclusions.
It's not just enough to say here's the results, but in many cases you need to provide enough data,
enough context that they not only see what you learned, but they see why you believe it is true.
And presentation with integrity shows the reader and really makes it clear to the reader what we learned.
The evidentiary support behind it, why it flows from the data.
Whereas dishonest presentation manipulates them into the conclusion without having the rigorous foundation underneath it.
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.
They presented campaign ad hundred network television, a graph that's purporting to show Kofod cases in various high population counties over time.
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,
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.
Then May 1st. Then April 30th. At the end we have May 2nd.
May 7th. April 26. May 3rd. It violates the expected convention.
And what they're what you'd need to do to show a trend over time that time goes from left to right.
They're sorting things in. They're putting things out of order to show the trend.
They want. In a way that's not substantiated by the data and it takes a lot of work to make a chart.
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,
but it's an example of one of the things that can happen when where when we focus on.
The effect we want to demonstrate over the evidentiary support for it.
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.
And these were a series of charts for an exhibition to show the economic, educational,
etc. progress of black Americans from emancipation to the turn of the century.
And he made a series of charts showing economic situations and things.
And here's a bar chart that clearly shows the result.
The distribution of economic statuses for farmers after a year of farm labor.
And it shows we have the first categories of bankrupt and in debt.
And then we have four different or five different levels of of non-negative return up to clearing.
Fifty dollars or more. And it shows them the bars are proportional to the length of the data.
Very clearly highlights these things.
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.
And we can see that even if you if you add all of that, they non-negative bars together.
It's not as many farmers as as the indebt category.
That's not a very standard thing. These charts were hand drawn.
But it's a creative use of the visualization to highlight in a way that's supported by the data,
the relative distributions of a different in return levels for black American farmers at the time.
Another one, that's another one that's creative here is this spiral bar chart.
Again, it's an unusual thing, but the lines, we can see them going progressively longer.
And if this were just a part of normal horizontal bar chart without the spiral that the smallest line,
the first line, 1975, would be so small you couldn't even see us.
This gives more space. And it shows good visualization doesn't just mean following the checklist of rules.
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.
There's no visual tricks to make things look larger or smaller than they are.
It transparently shows the connection between the conclusion and the underlying data that support it.
So to wrap up, the goal of good presentation is to guide the reader to what we learned and how we know it.
Effective. Presentation is going to highlight the important things for the reader to understand without distraction or deception.
And we're going to see throughout more of this week and throughout more the semester how practically to go about doing that.