As a junior researcher, there is one specific moment that consistently makes my heart race–the second before I click “run” on a new regression model.
Technically, running a regression isn’t the hard part. Once the data is cleaned and the code is written, the software does the heavy lifting in milliseconds. The true difficulty lies in the emotional weight of what comes next. In our “publish or perish” culture, we are often conditioned to believe that insignificant results are a personal failure. Even if we know that a null result tells an important story, the reality of the journal market can make those results feel like a dead end.
For months, this anxiety kept me up at night. I found myself spiraling into weeks of worry, wondering if my projects—and, by extension, my career—were destined to fail. Recently, however, I’ve started practicing a more grounded approach to my work by borrowing a few lessons from Stoicism.
The Dichotomy of Control in Research
At its core, Stoicism is about the “Dichotomy of Control”: distinguishing between what is up to us and what is not. Worrying about things outside our control is a recipe for misery; focusing on what we can influence is the path to peace.
When I applied this to my research, I realized how much of the “insignificance” I was stressing over was completely out of my hands:
- The Data Generation Process: We cannot force the world to behave the way we want it to. The data is what it is.
- Methodological Rigor: To be taken seriously, we must follow the established empirical models and procedures in our literature. We don’t have the “privilege” to ignore these standards just to hunt for a lower p-value.
- The Final Output: If I follow the literature rigorously and the data doesn’t yield a significant coefficient, that is simply the reality of the experiment.
I realized that if I have followed the best practices of my field, I have done my job. The rest belongs to the data.
Reframing “Success” as Skill Acquisition
If we can’t control the results, how do we stay motivated? I’ve found that the key is to shift the goalpost from the outcome to the process.
I have recently started a project regarding global sustainability disclosures. I honestly don’t know whether the final results will be “publishable” in a good journal. If I value only the project according to the p-value, I’m setting myself up for high-stakes gambling.
Instead, I’ve chosen to focus on what I am gaining regardless of the result:
- Technical Proficiency: I am learning how to navigate complex datasets like Datastream and Worldscope.
- Methodological Growth: I am discovering how to construct country-level indicators for sustainability mandates—a skill I can use for the rest of my career.
- Intellectual Discipline: I am learning how to frame a question and execute a plan.
Even if this specific regression fails to find a significant relationship, I will emerge from the project as a more capable, skilled researcher than I was when I started. These skills are “capital” that I keep forever, regardless of what any single journal reviewer says.
A Reminder
I am still a beginner at the mindset setting, and some days the anxiety still creeps back in. But viewing research through a Stoic lens has helped me find a bit more sleep and a lot more peace.
If you are currently staring at a screen of “insignificant” stars (or a lack thereof), remember: your value as a scholar isn’t just in the results you find, but in the rigor of your work and the growth of your mind.
We can’t control the data but how much we learn from it.