u/Digitale-Learning

I've been learning statistics recently, and every so often I come across a concept that completely changes how I look at everyday information.

For me, realizing that correlation doesn't imply causation was one of those moments. Now I catch myself questioning headlines all the time.

I'm curious...

What's the one statistics concept that had the biggest impact on how you interpret data?

Could be something like Simpson's Paradox, regression to the mean, survivorship bias, confidence intervals, p-values, averages, or something else entirely.

I'd love to hear examples from real life rather than textbook definitions.

We've all heard the phrase "Correlation doesn't imply causation." Yet it's one of the most common mistakes people make—not just in statistics classes, but also in business, healthcare, finance, social media, and even scientific research.

https://preview.redd.it/0c9yqgg2rx9h1.png?width=1536&format=png&auto=webp&s=672d09aad7603898e5c00324a931cd8b7693ede8

Sometimes two variables are so strongly correlated that it's incredibly tempting to conclude that one causes the other.

For example:

• Ice cream sales and drowning incidents 📈
• Coffee consumption and productivity ☕
• Screen time and depression 📱

In each case, the relationship is more complicated than it first appears.

I'm curious to hear from statisticians, data scientists, researchers, and anyone who works with data:

• What's the most convincing example of correlation being mistaken for causation that you've come across?
• Have you ever seen a company or organization make an expensive decision because they confused the two?
• What statistical methods or study designs do you rely on when trying to establish causality instead of just correlation?
• Are there any famous research papers or real-world case studies that perfectly illustrate this mistake?

Whether it's a funny example, a research study, or a real business story, I'd love to hear it.

Let's build a collection of the best examples—and maybe help a few people avoid one of the biggest pitfalls in statistics.

What's the biggest "Correlation ≠ Causation" mistake you've seen in real life?

I’ve been exploring Six Sigma, Lean Manufacturing, and basic statistics recently, and I noticed that a lot of concepts feel complex at first but become much easier once you see real-world examples.

For me, things like Pareto (80/20), 5 Whys, and process mapping started making more sense when I applied them to everyday problems. Curious to know from others: What helped you understand Six Sigma or Lean concepts more easily?

Was it: - Real-life examples? - Practical projects? - Certifications or courses? - Or something else? Would love to hear your experiences.

I’ve been revisiting some basic statistics concepts recently, and something about averages has been bothering me.

For example: If one person earns a huge salary and most people earn much less, the average still looks “high” — but that doesn’t really reflect reality for most people.

Same thing happens with: - Test scores - Waiting times - Data trends In theory, the average seems simple, but in real life it sometimes feels misleading. I know concepts like median and distribution exist, but I’m still trying to build an intuitive understanding.

How do you personally think about this? When should we trust the average, and when should we not?

Would appreciate simple explanations or real-world examples.

I’ve been exploring Lean tools like 5 Whys, waste reduction, and process mapping.

They made more sense when I saw real-world use instead of theory.

How did you learn Lean effectively? Any practical tips or experiences?

I’ve been learning Six Sigma recently and noticed that concepts like Pareto, 5 Whys, and process mapping feel complex until you actually apply them. For those who have experience: What helped you really understand Six Sigma? Real projects? Examples? Certifications? Would love your thoughts.