Maths should not be scary

In the social sciences, there seems to be a pervasive phenomenon among young scholars and students: the fear of numbers. This phenomenon is often perceived as an insurmountable obstacle for people with a qualitative background and sometimes prevents promising young students and scholars from exploring new ways of doing research, breaking boundaries (personal and academic), and doing their best work.

We all have met someone (or have been that person) who says “I would like to do this for my project, but it seems too complicated”, “This methodology works well for my research design but I have never used it before”, “I am not good enough at numbers to understand what is going on”.

I have been struggling with this too. Coming from a History major background, I never imagined I'd spend my career analysing data and running statistical models. Research seminars made it worse. When discussions turned to 'how did you cluster your standard errors,' I'd sit there feeling like the only person in the room who had no idea what anyone was talking about (to be honest, I am still not really sure what is going on with clustering).

After many discussions with other junior academics about wanting to use specific methods but doubting whether they could pull it off, I realised this might be something that people might relate to. So here's my highly opinionated and rough take on why it is normal to be scared of maths, but you shouldn't feel any less entitled to read, write, or teach numbers. (And by numbers, I mean math, calculus, coding, and everything related to quantitative social sciences.) I hope this resonates with scholars from underrepresented groups in quantitative political science as a call to join forces: to reclaim our space and embrace our right to produce exceptional research.

To be sure, by no means do I think numbers are necessary for political science and social sciences research. Some of the most interesting questions can be answered with qualitative methods. It just so happens that the type of questions I am interested in can be answered, mostly and more effectively, by numbers.

A math brain?

Do you remember the myth from childhood that some people are just "wired" to understand math while others aren't? I was labelled a "math kid" probably on the second day of elementary school. It was my thing. I developed my entire identity around being "good with numbers."

I then chose what's called Liceo Scientifico in Italy. In this high school stream, education focuses on maths, physics and natural sciences (interesting how the tables have turned in hindsight.).

My high school career went relatively smoothly until I was introduced to what to this day are my biggest nightmares: integrals and derivatives. It was the most traumatic experience of my academic life. After which, I decided to study history during my bachelor's degree to escape numbers as much as humanly possible. Soon after, however, I discovered my love for political science.

And boom: numbers again. It felt like I had to restart this fight with numbers from square one. They weren't just challenging; they were actively stopping me from studying the questions I was most passionate about.

How to overcome the fear of numbers

To be honest, I haven't completely overcome it. When I'm in a room and someone presents a complex estimation equation, impostor syndrome kicks in, and all I want is to leave the room as fast as possible. When I'm presenting my work and someone from the back of the room asks a methodological question, I feel my heart drop and think, "That's it, they got me." When a student looks at me with confusion in their eyes, hoping for a different explanation of what a p-value is, math still feels scary.

But six realisations have made math less intimidating for me:

Math is like a language: you don't need to be fluent to understand what's happening. You just need enough vocabulary to navigate the conversation. Think about travelling to a foreign country, you don't need to write poetry in the local language to get around and have meaningful experiences.

Even experts aren't 100% sure what's going on. Most people don't know what they're talking about completely! They have an intuition of the mechanism, but they don't understand every detail. That intimidating professor? They're probably winging it in certain areas too. That brilliant colleague publishing quantitative papers? I guarantee they just Googled basic formulas.

Doing math is key. Start using numbers! The more you practice a language, the more you learn it. The same goes for numbers: you need to use them to actually understand how they work. The first time I truly understood RDDs (Regression Discontinuity Designs) was by writing a paper about them. Not by reading about them or attending lectures, but by actually implementing one myself, making mistakes, and figuring it out along the way.

Making mistakes is key to learning. This is related to the previous point; you will never learn if you don’t make mistakes. The difference between those who move forward and those who stay stuck isn't natural ability—it's having just about enough confidence to make mistakes. I've watched colleagues with far less mathematical background than I successfully complete quantitative projects simply because they weren't paralysed by fear. They asked "dumb" questions, made errors and fixed them, collaborated with others, and persisted despite feeling inadequate.

There is no harm in asking for help. In fact, it's essential. This might be the most important lesson I've learned. Asking for help should not be seen as weakness, since the reality is that collaboration and help-seeking are fundamental to how research actually works. I used to think that asking for help with statistics meant I was a fraud. Now I realise that every successful researcher I know has a network of people they turn to for different things.

The truth is, people generally want to help. Most academics remember their own struggles with quantitative methods and are happy to pay it forward. Moreover, I stand by the fact that explaining things to others actually helps the explainer solidify their own understanding.

Start small with your help-seeking. Ask a colleague to look over your analysis plan before you start. Request a brief meeting to discuss whether your approach makes sense.

Numbers belong to everyone. Quantitative methods shouldn't be an Old Boys Club. The perspective that scholars from underrepresented groups bring—different ways of thinking about problems, asking questions, and interpreting results—is exactly what the field needs. When we allow artificial barriers to keep diverse voices out of quantitative work, we drain the entire discipline.

Breaking Free from Number Anxiety

Here's my advice for anyone struggling with number anxiety:

Start small. Pick one concept or method you need and focus just on that. Don't try to learn all of statistics at once. Get some data, clean it, and see what happens.

Find allies. Connect with colleagues who can help explain things without judgment. Sometimes, having someone translate concepts into plain language makes all the difference.

Remember, it's a tool, not an identity. Your worth as a scholar isn't tied to your ability to understand complex equations. Numbers are just one tool in your research toolkit.

Accept that you don't need to know everything right here, right now. Academic careers are long, and no one expects you to know everything at the beginning. The people you admire built their knowledge incrementally, one confused attempt at a time.

Don't let the fear of numbers stop you from pursuing what you're passionate about. Jump in, make mistakes, and remember—most of us are just figuring it out as we go along. The difference is that some people look confident while doing it, and that's okay, too.

Lastly, as scholars and teachers, we have the privilege and duty of creating learning environments where everyone feels entitled to give maths a try, and where this feels accessible to everyone.

This means actively dismantling the artificial barriers that keep diverse voices out of quantitative work. We need to normalise asking questions, making mistakes, and seeking help rather than perpetuating the myth that mathematical ability is innate or that struggling means you don't belong. When we create spaces where curiosity is valued over perfection. The field desperately needs the fresh perspectives, creative problem-solving approaches, and critical questions that scholars from all backgrounds bring.

Practically, this looks like explaining concepts without jargon, sharing our own struggles and uncertainties, and being transparent about how much we all rely on collaboration and resources. It means acknowledging that the intimidating facade of mathematical expertise often masks the same insecurities we all feel. When we model vulnerability and persistence in our own learning, we give others permission to do the same.

The goal isn't to convince everyone they need to become quantitative researchers—as I've said, numbers aren't necessary for all political science and social science research. Rather, it's to ensure that when people do want to engage with quantitative methods, they don't face unnecessary obstacles based on who they are or where they come from. We have the power to make math feel less like an exclusive club and more like a tool that anyone can learn to use in service of the questions that matter to them.

In the end, democratizing access to quantitative methods isn't just about fairness, it's about producing better research. When we expand who gets to ask questions with numbers, we expand what questions get asked at all.