Lying with Statistics

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”Good morning class”, greeted Professor Hodge her students. ”Last lesson, we talked about how to properly present the data you got from your experiments. Today, I’ll show you what kind of presentation you might encounter, because the presentation of data always has a goal. And it is important that you can see behind the intentions of the writer.”

She pressed a button on her computer and a picture was projected behind her.

”Now, that’s a graph you want to present your boss, or the shareholders of your company! What an increase in profit! This is clearly the effect of your great work. Well…”
The picture was replaced by a different one.

”Some of you might already have noticed that, in the first picture, the difference between last year’s and this year’s profits was just about $20. In this pie chart it’s obvious that when you look at the overall profit, both years brought in almost the same amount of money. This kind of exaggerated scaling can be used for basically everything, because people rarely look at the numbers. The size difference of the bars is way more important, because it’s easier to process.”

”This technique of an adjusted scale can be used in a number of ways. It all depends on the data you present – and what you want the reader to feel when they have a look at it.”

”Don’t get me wrong”, Hodge said. ”A bigger scale is not always the right choice. But it happens a lot that people try to create a dramatic effect and that is achieved best by making drops and rises seem more drastic. But graphs are not the only thing that can be used to create a false impression. Please look at this statement.”
A new slide appeared.

A study showed that in almost fifty percent of cases, women were more likely to be the offender in a violent crime.

”Now, look at the actual data.”

Crime	Female offenders	Male offenders
Murder	3 %	97 %
Spousal Abuse	51 %	49 %
Rape	20 %	80 %
Mass Shooting	10 %	90 %
Burglary	5 %	95 %
Terrorism	50.5 %	49.5 %
Drug crimes	55 %	45 %
Kidnapping	52 %	48 %
Carjacking	30 %	70 %

”Nine different kinds of violent crimes and women commit a higher number of them in 4 cases. That is about 44 %. So yes, almost 50 %. But look at the exact numbers! In the cases when women are the main offenders, the percentage of male offenders is almost the same. The highest difference is 4 % while the highest difference for crimes mostly committed by man is at 95 %. That paints a completely different picture, doesn’t it? And still, the sentence from before immediately makes you think about how women are just as violent as men, in every regard.”

At this point, the class started to become agitated. One of the male students raised his hand.

”Why should anyone write something like this in a scientific paper?” He asked. ”I can understand that mainstream media would do something like this, but it’s just illogical for a researcher to present data in a way that totally takes it out of context.”

Professor Hodge smiled.

”But what is the context? Whatever research you do, there will always be something you look for. And what happens when you don’t get the results you are looking for? Or, almost worse, you almost get the results you want? Do you know what the saddest thing is that you can read in a scientific paper?”

She waited a few moments, in case anyone wanted to answer the question. Nobody did.

”The saddest thing to read is the collected results were not statistically relevant. You can almost hear the pain the researcher experienced while writing this. This person actually got data out of an experiment or a study, but was not able to use it because the statistical relevance wasn’t there. Every result could be just random. Do you have any idea how frustrating that can be?”

Now, one of the students raised their hand.

”Then why to people test for statistical relevance? Wouldn’t it be easier to just write about the results and ignore the possibility that it isn’t relevant at all?”

”Some people do”, Hodge explained. ”But that is the beauty about peer-reviewed papers. When other people check your results, they try to find inconsistencies. And the people reviewing your work are at least as qualified to interpret the data as you are. So the only people that really get away with actively manipulating their data are those that don’t depend on peer-reviewing.”

The Professor looked around. Her gaze met many worried faces.

”There will always be articles that pretend to be scientific. They will use exaggerated graphs, vague wording and statistically insignificant data. But if you’re willing to read into it deeper and know how to separate a well written article from one that just wants to push an agenda, you’ll find the truth. It is a lot of work but choosing the easy path will only make you believe in the wrong things. And then you’ll end up not vaccinating your child against measles because you think there is mercury in the vaccine.”

Some students chuckled. Hodge looked at her clock.

”Alright, class dismissed. I will see you next week.”

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Sources:

Images were created by me using Excel.
The data used is fictional.

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