1 00:00:07,808 --> 00:00:10,839 A toothpaste brand claims their product will destroy more plaque 2 00:00:10,839 --> 00:00:12,910 than any product ever made. 3 00:00:12,910 --> 00:00:16,411 A politician tells you their plan will create the most jobs. 4 00:00:16,411 --> 00:00:18,951 We're so used to hearing these kinds of exaggerations 5 00:00:18,951 --> 00:00:20,850 in advertising and politics 6 00:00:20,850 --> 00:00:23,131 that we might not even bat an eye. 7 00:00:23,131 --> 00:00:26,111 But what about when the claim is accompanied by a graph? 8 00:00:26,111 --> 00:00:28,471 Afterall, a graph isn't an opinion. 9 00:00:28,471 --> 00:00:32,611 It represents cold, hard numbers, and who can argue with those? 10 00:00:32,611 --> 00:00:36,403 Yet, as it turns out, there are plenty of ways graphs can mislead 11 00:00:36,403 --> 00:00:38,192 and outright manipulate. 12 00:00:38,192 --> 00:00:40,745 Here are some things to look out for. 13 00:00:40,745 --> 00:00:45,760 In this 1992 ad, Chevy claimed to make the most reliable trucks in America 14 00:00:45,760 --> 00:00:47,510 using this graph. 15 00:00:47,510 --> 00:00:51,963 Not only does it show that 98% of all Chevy trucks sold in the last ten years 16 00:00:51,963 --> 00:00:53,592 are still on the road, 17 00:00:53,592 --> 00:00:57,338 but it looks like they're twice as dependable as Toyota trucks. 18 00:00:57,338 --> 00:01:00,634 That is, until you take a closer look at the numbers on the left 19 00:01:00,634 --> 00:01:05,472 and see that the figure for Toyota is about 96.5%. 20 00:01:05,472 --> 00:01:09,313 The scale only goes between 95 and 100%. 21 00:01:09,313 --> 00:01:12,963 If it went from 0 to 100, it would look like this. 22 00:01:12,963 --> 00:01:16,243 This is one of the most common ways graphs misrepresent data, 23 00:01:16,243 --> 00:01:18,333 by distorting the scale. 24 00:01:18,333 --> 00:01:20,804 Zooming in on a small portion of the y-axis 25 00:01:20,804 --> 00:01:25,703 exaggerates a barely detectable difference between the things being compared. 26 00:01:25,703 --> 00:01:27,974 And it's especially misleading with bar graphs 27 00:01:27,974 --> 00:01:31,023 since we assume the difference in the size of the bars 28 00:01:31,023 --> 00:01:33,233 is proportional to the values. 29 00:01:33,233 --> 00:01:36,125 But the scale can also be distorted along the x-axis, 30 00:01:36,125 --> 00:01:40,414 usually in line graphs showing something changing over time. 31 00:01:40,414 --> 00:01:44,747 This chart showing the rise in American unemployment from 2008 to 2010 32 00:01:44,747 --> 00:01:47,996 manipulates the x-axis in two ways. 33 00:01:47,996 --> 00:01:50,395 First of all, the scale is inconsistent, 34 00:01:50,395 --> 00:01:53,416 compressing the 15-month span after March 2009 35 00:01:53,416 --> 00:01:56,755 to look shorter than the preceding six months. 36 00:01:56,755 --> 00:02:00,106 Using more consistent data points gives a different picture 37 00:02:00,106 --> 00:02:03,705 with job losses tapering off by the end of 2009. 38 00:02:03,705 --> 00:02:06,675 And if you wonder why they were increasing in the first place, 39 00:02:06,675 --> 00:02:10,615 the timeline starts immediately after the U.S.'s biggest financial collapse 40 00:02:10,615 --> 00:02:12,626 since the Great Depression. 41 00:02:12,626 --> 00:02:15,219 These techniques are known as cherry picking. 42 00:02:15,219 --> 00:02:18,869 A time range can be carefully chosen to exclude the impact of a major event 43 00:02:18,869 --> 00:02:20,648 right outside it. 44 00:02:20,648 --> 00:02:24,762 And picking specific data points can hide important changes in between. 45 00:02:24,762 --> 00:02:27,356 Even when there's nothing wrong with the graph itself, 46 00:02:27,356 --> 00:02:30,937 leaving out relevant data can give a misleading impression. 47 00:02:30,937 --> 00:02:33,997 This chart of how many people watch the Super Bowl each year 48 00:02:33,997 --> 00:02:37,626 makes it look like the event's popularity is exploding. 49 00:02:37,626 --> 00:02:40,198 But it's not accounting for population growth. 50 00:02:40,198 --> 00:02:41,967 The ratings have actually held steady 51 00:02:41,967 --> 00:02:45,109 because while the number of football fans has increased, 52 00:02:45,109 --> 00:02:47,959 their share of overall viewership has not. 53 00:02:47,959 --> 00:02:49,888 Finally, a graph can't tell you much 54 00:02:49,888 --> 00:02:53,318 if you don't know the full significance of what's being presented. 55 00:02:53,318 --> 00:02:56,457 Both of the following graphs use the same ocean temperature data 56 00:02:56,457 --> 00:02:59,719 from the National Centers for Environmental Information. 57 00:02:59,719 --> 00:03:02,490 So why do they seem to give opposite impressions? 58 00:03:02,490 --> 00:03:05,279 The first graph plots the average annual ocean temperature 59 00:03:05,279 --> 00:03:07,987 from 1880 to 2016, 60 00:03:07,987 --> 00:03:10,149 making the change look insignificant. 61 00:03:10,149 --> 00:03:12,878 But in fact, a rise of even half a degree Celsius 62 00:03:12,878 --> 00:03:15,799 can cause massive ecological disruption. 63 00:03:15,799 --> 00:03:17,219 This is why the second graph, 64 00:03:17,219 --> 00:03:19,858 which show the average temperature variation each year, 65 00:03:19,858 --> 00:03:22,390 is far more significant. 66 00:03:22,390 --> 00:03:27,379 When they're used well, graphs can help us intuitively grasp complex data. 67 00:03:27,379 --> 00:03:31,180 But as visual software has enabled more usage of graphs throughout all media, 68 00:03:31,180 --> 00:03:35,900 it's also made them easier to use in a careless or dishonest way. 69 00:03:35,900 --> 00:03:39,560 So the next time you see a graph, don't be swayed by the lines and curves. 70 00:03:39,560 --> 00:03:40,882 Look at the labels, 71 00:03:40,882 --> 00:03:42,130 the numbers, 72 00:03:42,130 --> 00:03:43,048 the scale, 73 00:03:43,048 --> 00:03:44,360 and the context, 74 00:03:44,360 --> 00:03:46,780 and ask what story the picture is trying to tell.