Wednesday Wisdom: Science of B2B Statistics Glossary - Correlations

Hi all,

Not our typical #WednesdayWisdom reading recommendation, but I wanted to highlight a resource from the research team: our Statistics Glossary. We created it to help you better understand our research and grasp common statistics we encounter in both professional and personal contexts. It’s designed for everyone, whether you're familiar with stats or not, to get more out of our insights.

We’d love your feedback! Are our explanations clear? Any questions you’ve always had about certain terms? Let us know in the comments or DM us directly.

Today, I’ll share a term you’ve probably heard out in the wild 😜: Correlation. Here’s a condensed version of how we define it in the glossary:

• Correlation analysis measures whether changes in one thing are reliably associated with changes in another. 
• 📉Negative Correlations: When an increase in one variable is associated with a decrease in another. Example: In our B2B Buyer Experience Research, we found that as the importance of a solution increased, the buying journey tended to be shorter. Importance didn't cause a faster cycle, but they consistently moved together.
• 📈Positive Correlations: When both variables move in the same direction. Example: In our B2B marketer compensation research, marketers with longer company tenure reported needing higher compensation to switch jobs.
• 🚨Correlation ≠ Causation: What correlations do not indicate is that one thing causes another. In the example above, while longer tenures tended to coincide with higher compensation expectations for switching companies, tenure itself didn’t cause the increased salary expectations, nor did higher salary expectations cause longer tenure. During longer tenures, employees are likely to be establishing friendships, building loyalty to the organization, developing expertise, etc. —factors that can influence their expectations for switching jobs. However, it is not tenure itself driving the increase in required compensation. Similarly, in everyday life, when the sun is shining, more people tend to be at the park. But the sunshine itself doesn’t cause people to go—because the sun is shining right now, and I am very much not at the park (though I’d love to be!). 🙂

This is just one example from our glossary—there are plenty more terms to explore! We hope this helps you engage more deeply with our research and develop a sharper eye for interpreting stats in other research you come across.

Are there any terms you’ve come across that you’d like me to highlight in a RevCity post? Let me know!

@Kerry