![]() ![]() d) Focus on One StoryĪt this point I need to narrow down my context and define what questions I want to ask and which metrics will answer those questions. So the two confounding variables we found for the race time paradox, as we could call it, were both gender AND year. AND we can see the US, for both males AND females, haven’t won gold medals in over a decade. Looking at the above graph, we can see the overall finish times have decreased over time. But I’ve changed the red color to highlight the US and shades of gray to put the other countries in the background. In the above graph, each country is now represented by a line graph (or two lines, if they both females and males won gold medals for that country). Is the YEAR a factor? When did the US win gold medals? But this doesn’t explain why the US has taken home so many more gold medals than other countries, while the overall average finish times for US finishers (and gold medalists too) are slower! What’s going on? And 4 of those 9 countries ONLY have female representation at gold. Wow, interesting! Of the 9 countries with gold medals, 8 of them have female representation on the 1st place podium. csv form for a 2019 Makeover Monday challenge. ![]() I must give credit to Eva Murray and Andy Kriebel for putting this data into. Here I have a dataset from Wikipedia – Ironman World Championship Medalists. See Anscombe’s Quartet for a demonstration of WHY. There are so many things a chart or graph (or multiple charts and graphs) can tell you about the data that tables and summary statistics cannot (including errors). When I taught AP Stats I told the students the same thing I tell you now: When you get your hands on a set of data, MAKE A PICTURE. And that begins with the exploratory analysis of your dataset – which should always begin with exploring your data visually. To tell a data story, you have to find the data story. ![]() Find the Data Story: Exploratory Analysis a) Make a Picture If you know your audience’s goals, you can more easily cancel out the noise in your data and define the right questions and metrics along the way. And you’ll need to continue circling back to your audience throughout all of the steps below. Define Your Audience and Determine Their Objectives Here I will map out a few general steps for both exploratory and explanatory analysis to help you simplify the complex both in process and in message. And since your and your audience’s interests, background, and ability to draw conclusions plays into your storytelling, this process of finding and telling a data story could easily detour and fall into rabbit holes. There are a few steps to telling a data story, and they could get complicated depending on your data type and analysis. That action might be a business decision or a “wow, I now appreciate this topic” response, depending on the context and audience. In my humble opinion, data storytelling takes the data (aka numbers in context) and, not only translates it into consumable information, but creates a connection between the audience and the insights to drive some action. I received some great answers to this tweet here. ![]()
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