“Don’t trust any statistic you didn’t fake / manipulate yourself.” I guess most of us have heard statements like that. And it annoys me more and more. It’s often used just asa joke when it’s obvious that a statistics isn’t too easy to interpret – but I see this phrase more and more being used deliberately to produce fake news and to manipulate.
The Danger of Context-Free Data
Statistics are meant to help us understand the world, but when taken out of context, they often cause the opposite. The relevance of context and definition once struck me literally in the face when I saw a statistics about train punctuality (yeah well a running gag in Germany):
A report sayed 9x% of trains were “on time.” Okay. But what does “on time” even mean? Is a train on time if it’s 1 minute late? 5 minutes? 10? When I noticed that this wasn’t mentioned in the report, I realized that the number above was absolutely useless to me. Yes it’s annoying, but we need to be clear about what we’re talking about, because everyone has a different meaning of “on time”. And just to underline this: we don’t even need to agree on the particular definition of “on time”. But by having the definition, we at least start to have a common ground for a discussion.
Another example: We once analyzed forum response times. The result: “On average, users get a first reply within a couple of hours.” That did sound way off. So I took a closer look and found that outliers weren’t treated at all in the data set. Responses that were given years(!) after the original question totally skewed the data set. Once we took those outliers into consideration, we could say something like “98% of answers are given within a few minutes”. Did we fake the stats? No. A simple average was just simply the wrong type of answer in this context.
The Trick of Oversimplification: “Faking Statistics”
The phrase “faking statistics” might sound like a joke, and it often is (hopefully at least). But when used deliberately, it can serve a malicious purpose. The then goal isn’t to question the numbers themselves – it’s to distract you from the bigger picture. By simplifying things down to a joke, it becomes easier to convince people to distrust this “overly complicated” explanation and illudes the world is simpler than it really is.
This oversimplification makes us feel like everything has an easy solution. But let’s face reality: todays world is complex! Global economy, international contracts, multilateral interests, lobbys, contracts with non-oublic (but important) clauses, … But we don’t even need to go that far. Let it just be regulations or laws that prohibit or enforce certain things. We aren’t living in the middleages any more (well – luckily). But the price for a lot of wealth and comfort is a huge amount of complexity in our world.
When we see a very simple statistics / conclusion, it is often missing key details that could change everything. So while the “faking statistics” line might get a chuckle, it’s also a clever way to shift focus away from the more complicated, uncomfortable truth.
Reflect on What It Does to You
When you hear that sentence netxt time, do me a favor and pay close attention to how it affects you. What happens to your mind?
Do you still see the stats the same way? Or does it suddenly feel less credible, even though it’s solid? Do you start to doubt it, joke about it, or disbelieve it? This is exactly what populism does. It plants a seed of doubt, making us question the things we should trust.
Populism doesn’t always rely on lies. It thrives on creating uncertainty and undermining our faith in facts. By making us second-guess what we see, it makes us stop asking the right questions and start buying into oversimplified explanations.
So, next time you hear that sentence, reflect: How does it make you feel? Are you questioning the stat, or just following the simplification narrative?
The Bottom Line
Well -unfortunately there is no silver bullet (no oversimplified solution). Healthy skepticism? Good. Blind cynicism? Not so much. Numbers can reveal truth – but only if we ask the right questions.
So, the next time someone throws a “Don’t trust any statistic you didn’t fake yourself” at you – be cautious. Someone might want to trick you.