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The Lying With Statistics

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When given any information, you’d hope for it to be true. Well, what if there was a way that there could be many forms of giving you information, to make it seem to be true, but could be messing with you. This is known as deceiving. A very common area for this to be used is in statistics. Many staticians use Statistics as a way to get people to view something a certain way, when in reality they are misleading them to something that isn’t necessarily the whole truth. “So it is with much that you read and hear. Averages and relationships and trends and graphs are not always what they seem.” Meaning that all those things, are not completely honest, and they’re a trick. Before going into specifics of lying with statistics, I’m going to bring a little bit of christianity into it.

To begin, the way that statistics can be used to manipulate is very intriguing, and interesting, but that doesn’t make it right. Lying is a sin. In Proverbs 12:22 it states; The lord detest lying lips, but he delights in people who are trustworthy. This verse is about lying, and is saying how the Lord despises those who lie. Lying is no way to live your life, it is bad not only for other people, but for those who are lying themselves. Leviticus 19:11 “You shall not steal; you shall not deal falsely; you shall not lie to one another.” (The Holy Bible). The key part to that verse is “you shall not deal falsely”. They are not saying you can’t deal at all, but if you do it should be done truthfully, and honestly, not through lies, and deceit. Now into how it is really done. The first statistics method I’m going to bring up is bias in sampling.

It’s very common to see bias in sampling. The example given in the book How to Lie with Statistics was average Yaleman class of ‘24 makes a year. The number for that was $25,111. One of the first things that stands out, is that it is very unlikely that the known average is going to be known down to the exact dollar amount. What is even more undependable is that these averages are made from what the average yale men claim that they earned. Meaning they could have lied about what they make, they could have underestimated or overestimated by accident what the make. Also just because that is the average, does not mean that’s what you’ll receive, it’s actually believed that some of those mens incomes may be nearer half of that amount. Which goes to show that numbers don’t mean as much as they think you do, you have to see the process behind how they got those numbers, and make sure that how they produce and display the numbers are truthful. None of it is 100% sure that it is correct. Which makes it extremely confusing to viewers eyes. People could see this and believe that they would make that if they went to Yale and graduate, but in reality there is no proof, or factual evidence that that is the correct average for how much money they make a year.

Which is why survey bias is one of the biggest ways of deceiving the public. This type of bias is known as built-in bias. The next form of trickery in statistics is in graphing. It’s how people can be fooled by the eyes thinking that they are seeing the truth of one thing, but the creator of the graph is really just enhancing their side of it to make them seem better, or vise versa, the other seem worse. This chapter of the book is called The Gee-Whiz Graph. One of the main things that stands out is how just simply changing the numbers on the graphs can completely change the pictures. Like if you start at 0 and go to a high number there won’t be a big noticeable difference on the graph. Perhaps you make it a smaller range with the same amount of line, you start to be able to identify and notice every single line, and movement of what happened during that time. This simple change can go from making the graph look like a mostly straight and steady incline to looking like a completely new graph full of curves and new points. That’s why it is very important to make sure you notice types of graphs and analyze them well.

Lastly, one of the most common items in statistics are pictures. People tend to represent information with pictures as opposed to graphs. Which is a great thing, because a lot of people are more visual and would rather see pictures then numbers, but it’s not good when they’re used in wrongful ways. In a graph, it may show how there’s a 50 percent difference between 2 different things, but when they go to represent that in a one-dimensional picture they tend to make the picture look larger than the other than more than 50 percent, and will make the picture look 1 and a half times bigger than the other, which makes it appear 3 times as much. This can be tricky to the eye because when they make the picture look bigger, it can seem like a lot larger of a difference than it actually is and plays tricks with your mind.

In summation, those are a few ways that statistics can be used to lie, and deceive. There are many more ways that you can use statistics to lie. The three most intriguing ones to me were sampling, graphing, and using one dimensional pictures. These ones stood out the most because they’re such smaller things, you wouldn’t expect them to be wrong or deceitful, but they can be. I find these the most interesting because they’re used so much and you don’t realize how much they’re being used until you read and learn about them, so once you do you start paying more attention to them, and finding them in everyday life. Sometimes you just have to look beyond just what is in front of you, and what you’re being told so you can get the absolute truth. You never know when someone, or something could be lying, or trying to deceive you, so pay close attention to what you see, and chose wisely what you believe.

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