Presenting Your Findings

Once you've found something in the data, 0:00 you probably want to start telling your co-workers. 0:02 But before you go telling your results to everyone you know, 0:04 there's one very important thing you need to know. 0:07 Correlation does not imply causation. 0:10 Here is a graph of ice cream sales versus crime rates. 0:13 It's clear that higher ice cream sales correlates with higher crime rates. 0:17 But that doesn't mean that higher ice cream sales causes more crime. 0:21 That'd be ridiculous. 0:24 When you're presenting your findings, you need to remember that even though two 0:27 things might be related, that doesn't mean they have anything to do with each other. 0:30 The most you can say is that they're correlated. 0:35 And on that note, I wonder what a graph of our age data would look like. 0:38 Let's hop back to Google Sheets to find out. 0:41 To make a graph of ages versus counts, let's select both the age and 0:45 count columns, including the headers. 0:49 And then let's click on the Insert Chart button. 0:53 And there we go. 0:57 Thanks to all these spikes, it's clear clear that some ages are overrepresented. 0:58 Let's drag this chart to the top. 1:03 And then since we don't really need this legend over here, let's remove it by 1:18 going into the chart editor, clicking the customize tab, selecting the legend and 1:23 for position, set it equal to none to remove the legend from the chart. 1:28 Perfect. 1:33 One thing you want to be careful of though, 1:35 is the charts can be unintentionally misleading. 1:36 Having all that data represented graphically 1:39 is a lot noisier than having it in text. 1:42 If you're trying to report something, 1:45 don't rely on others to interpret your graphs. 1:46 Make it easy for them by spelling out exactly what you found. 1:49 A good model to follow when explaining your findings is the outline of 1:53 a scientific article. 1:56 Start with an introduction where you introduce the problem and 1:58 how it came to be. 2:01 Then, formally state the hypothesis and 2:03 describe the procedure used to test that hypothesis. 2:06 Finally, you want to report the results of your testing and 2:09 then share any conclusions. 2:13 Here's what this might look like for the problem we just tackled. 2:15 Introduction, we've received complaints that some ages 2:19 have an easier time qualifying than others. 2:22 We aim to assess the truthiness of those claims. 2:25 Hypothesis, some ages have an easier time qualifying than others. 2:28 Procedure, to find this out, we looked at the number of qualifying runners for 2:33 adjacent ages. 2:37 If no ages have an advantage, 2:39 the difference between adjacent ages should be relatively small. 2:41 We picked a figure for what would be an unacceptable difference, and 2:44 then tested adjacent ages. 2:48 Results, we found four differences that exceeded our maximum. 2:50 Conclusions, we conclude that some ages have an easier time qualifying 2:54 than others. 2:58 Breaking it down this way makes it easy to understand what's going on. 3:00 There are many things you can do to present your findings, and 3:04 this is just one way. 3:06 But it's nice to have a format in mind, at least to get you started. 3:08 Another thing to mention is that unlike a scientific article, at the end, 3:12 you might want to include a recommended solution. 3:16 If you think you've found a solution to the problem, make sure to share it. 3:19 It really helps bring things full circle. 3:22 Also, before we go, 3:25 you should know that we haven't found anything particularly alarming here. 3:26 The Boston Marathon uses different qualifying times for different age groups. 3:30 So it's sort of an open secret that you'll have an easier time qualifying 3:34 as a 35 year old instead of a 34 year old. 3:38 So maybe our recommendation would be to get rid of age groups and, 3:41 instead, have a different qualifying time for each age. 3:45 There's so much you can do with data analysis, 3:49 from figuring out which peanut butter to buy to finding a good deal on a house, 3:51 data analysis informs our every decision. 3:55 But this is just the beginning, there's lots more to learn about data analysis. 3:58 Until next time. 4:03