7: A toothpaste brand claims their product will destroy more plaque

10: than any product ever made.

12: A politician tells you their plan will create the most jobs.

16: We're so used to hearing these kinds of exaggerations

18: in advertising and politics

20: that we might not even bat an eye.

23: But what about when the claim is accompanied by a graph?

26: Afterall, a graph isn't an opinion.

28: It represents cold, hard numbers, and who can argue with those?

32: Yet, as it turns out, there are plenty of ways graphs can mislead

36: and outright manipulate.

38: Here are some things to look out for.

40: In this 1992 ad, Chevy claimed to make the most reliable trucks in America

45: using this graph.

47: Not only does it show that 98% of all Chevy trucks sold in the last ten years

51: are still on the road,

53: but it looks like they're twice as dependable as Toyota trucks.

57: That is, until you take a closer look at the numbers on the left

60: and see that the figure for Toyota is about 96.5%.

65: The scale only goes between 95 and 100%.

69: If it went from 0 to 100, it would look like this.

72: This is one of the most common ways graphs misrepresent data,

76: by distorting the scale.

78: Zooming in on a small portion of the y-axis

80: exaggerates a barely detectable difference between the things being compared.

85: And it's especially misleading with bar graphs

87: since we assume the difference in the size of the bars

91: is proportional to the values.

93: But the scale can also be distorted along the x-axis,

96: usually in line graphs showing something changing over time.

100: This chart showing the rise in American unemployment from 2008 to 2010

104: manipulates the x-axis in two ways.

107: First of all, the scale is inconsistent,

110: compressing the 15-month span after March 2009

113: to look shorter than the preceding six months.

116: Using more consistent data points gives a different picture

120: with job losses tapering off by the end of 2009.

123: And if you wonder why they were increasing in the first place,

126: the timeline starts immediately after the U.S.'s biggest financial collapse

130: since the Great Depression.

132: These techniques are known as cherry picking.

135: A time range can be carefully chosen to exclude the impact of a major event

138: right outside it.

140: And picking specific data points can hide important changes in between.

144: Even when there's nothing wrong with the graph itself,

147: leaving out relevant data can give a misleading impression.

150: This chart of how many people watch the Super Bowl each year

153: makes it look like the event's popularity is exploding.

157: But it's not accounting for population growth.

160: The ratings have actually held steady

161: because while the number of football fans has increased,

165: their share of overall viewership has not.

167: Finally, a graph can't tell you much

169: if you don't know the full significance of what's being presented.

173: Both of the following graphs use the same ocean temperature data

176: from the National Centers for Environmental Information.

179: So why do they seem to give opposite impressions?

182: The first graph plots the average annual ocean temperature

185: from 1880 to 2016,

187: making the change look insignificant.

190: But in fact, a rise of even half a degree Celsius

192: can cause massive ecological disruption.

195: This is why the second graph,

197: which show the average temperature variation each year,

199: is far more significant.

202: When they're used well, graphs can help us intuitively grasp complex data.

207: But as visual software has enabled more usage of graphs throughout all media,

211: it's also made them easier to use in a careless or dishonest way.

215: So the next time you see a graph, don't be swayed by the lines and curves.

219: Look at the labels,

220: the numbers,

222: the scale,

223: and the context,

224: and ask what story the picture is trying to tell.