A toothpaste brand claims their product will destroy more plaque（匾） than any product ever made（使）. A politician tells you their plan will create the most jobs. We're so used to hearing these kinds of exaggerations（夸张） in advertising and politics that we might not even bat（用球棒击球） an eye. But what about when the claim is accompanied（陪伴） by a graph? After all, a graph isn't an opinion. It represents cold, hard numbers, and who can argue with those?

  Yet, as it turns out, there are plenty of ways graphs can mislead and outright（完全的） manipulate（操纵）. Here are some things to look out for. In this 1992 ad, Chevy claimed to make the most reliable trucks in America using this graph. Not only does it show that 98 percent of all Chevy trucks sold in the last 10 years are still on the road, but it looks like they're twice as dependable（ 可靠的） as Toyota trucks. That is, until you take a closer look at the numbers on the left and see that the figure for Toyota is about 96.5 percent. The scale（刻度） only goes between 95 and 100 percent.

  If it went from 0 to 100, it would look like this. This is one of the most common ways graphs misrepresent（歪曲） data（资料）—by distorting（扭曲） the scale（刻度）. Zooming in on a small portion（部分） of the y-axis（轴） exaggerates（夸大） a barely detectable difference between the things being compared. And it's especially misleading（ 令人误解的） with bar graphs, since we assume（假定） the difference in the size of the bars is proportional（比例的） to the values. But the scale can also be distorted along the x-axis, usually in line graphs showing something changing over time. This chart showing the rise in American unemployment from 2008 to 2010 manipulates the x-axis in two ways.

  First of all, the scale is inconsistent, compressing（压缩） the 15-month span（跨距） after March（三月） 2009 to look shorter than the preceding six months. Using more consistent（坚持的） data（资料） points gives a different picture, with job losses tapering（逐渐减少） off by the end of 2009. And if you wonder why they were increasing in the first place, the timeline starts immediately after the U.S.'s biggest financial collapse（倒塌） since the Great Depression（沮丧）. These techniques are known as "cherry（樱桃） picking." A time range can be carefully chosen to exclude（排除） the impact of a major event right outside it. And picking specific data points can hide important changes in between.

  Even when there's nothing wrong with the graph itself, leaving out relevant（相关的） data（资料） can give a misleading（ 令人误解的） impression. This chart of how many people watch the Super Bowl each year makes it look like the event's popularity（普及） is exploding. But it's not accounting（说明） for population growth. The ratings（等级） have actually held steady（稳固的）, because while the number of football fans has increased, their share of overall（全面的） viewership has not. Finally, a graph can't tell you much if you don't know the full significance（意义） of what's being presented. Both of the following graphs use the same ocean temperature data from the National（国民） Centers for Environmental Information.

  So why do they seem to give opposite impressions? The first graph plots the average annual（年度的） ocean temperature from 1880 to 2016, making the change look insignificant（无关紧要的）. But in fact, a rise of even half a degree Celsius can cause massive（厚重的） ecological（ 生态的） disruption（ 动乱）. This is why the second graph, which shows the average temperature variation（变化） each year, is far more significant（重大的）. When they're used well, graphs can help us intuitively（直觉地） grasp（抓紧） complex data（资料）. But as visual（视觉的） software has enabled（使能够） more usage（使用） of graphs throughout all media, it's also made them easier to use in a careless or dishonest（不诚实的） way.

  So the next time you see a graph, don't be swayed（摇动） by the lines and curves（曲线）. Look at the labels, the numbers, the scale and the context, and ask what story the picture is trying to tell.