I have written this post to breakdown jargon barriers in maths and stats for my Linkedin audience. I want it to be warm, relatable, and valuable. Without changing my style and tone, what is lacking to be representative of the top 1% writers on linkedin and valuable for aspiring data scientists or data analysts? Frustration is good, fear is good, failure is good, and disappointment is good. Need I say more? Anyone stating otherwise don't know what they are talking about. They aren't to blame though. It's a prevailing thought. This should be good news, by the way.

Unfortunately, because of these misconceptions, you can easily missed out on otherwise great opportunities.

Have you ever felt like you’ve landed on a foreign planet where everyone speaks a language full of strange symbols and words you don't understand?

If you’re venturing into data science or statistics, chances are you’ve met this alien language, filled with jargon like “p-value,” “sigma,” “eigenvector,” and dozens more that can make your head spin. Frightening, I know but really, it's a good thing as I said above.

It’s easy to feel lost, frustrated, or even a little defeated. But here’s the truth: those fancy words aren’t there to trip you up. They’re tools, and you can learn to wield them like a pro. say out loud, brilliant!


Meet Priya. New to data science, she was overwhelmed the first time she heard a “p-value” mentioned in a meeting. Was it a secret code? A magic number? She felt like everyone else understood, but she didn’t.

Priya’s turning point came when she started asking simple questions and searching for everyday explanations. She learned a p-value is just a number that helps you decide if a pattern in data is real or just a fluke. Suddenly, that alien word didn’t feel so scary.


Why does jargon feel so intimidating?
Jargon can seem like a secret code designed to exclude newcomers. When you hear endless unfamiliar terms, it’s easy to feel dumb or like you don’t belong. That fear slows down learning and builds walls where there should be bridges.

But jargon is really just a specialised vocabulary. Like learning any new language, it takes time and practice to feel fluent.


How do you decode the maths lingo?
Break terms down into everyday language. Instead of memorising definitions, try explaining what a term means in your own words. For instance, think of a “regression” as a line that helps predict a trend, not a mysterious formula.

Keep a personal glossary. Write down tricky terms with simple explanations you can revisit.

Use metaphors and analogies. Comparing a “confidence interval” to a weather forecast range can make the concept stick.

Ask questions without shame. Everyone starts somewhere, and experts love helping fresh eyes.

Make jargon your friend. Create your own shortcuts or memorable phrases that help you recall meaning.


Remember — becoming fluent in data science language is a journey, not a sprint. Celebrate the little wins — every time you understand a new term is a step forward.

Soon, those alien words will become familiar, like second nature.


Your Turn:

This week, pick three daunting maths or stats terms. Look them up, ask around, or find simple explanations. Then, try explaining them to a friend or write about them in your own words.

Share your discoveries or favourite “maths myths busted” in the comments. Let’s build a community where jargon isn’t a barrier — it’s a gateway.


Key Takeaways
Jargon in maths and stats can feel intimidating but is conquerable.

Breaking down terms into simple language builds confidence.

Metaphors and analogies make complex ideas relatable.

Asking questions accelerates fluency.

Maths language is a skill, and you can master it, one term at a time.