Part 2. Methodology
I am quite sure that I am not at a good enough position to give advice. My own trail turbulent, and, for a while, my spirits low. Still, situations like this allowed myself to take note of others’ experience, and my current issues awaiting attention.
I wish to discuss some common traps a beginner researcher (such as myself) might fall into. Many of them are not ill-intended, but represent logical fallacies when one only focuses on one side of the coin, so to speak. The list is based on a Zhihu post by xcsun, a Neuroscience PhD student that I follow from MIT.
In dealing with Authority figures
Ironically quoting Einstein on this, “Blind belief in authority is the greatest enemy of truth.”
One should listen what the authority is thinking, but should, at the same time, not compromise their independent thinking.
I realize that the stuff that I am beginning to read almost all contain big names in their authorship information, and consider this a symptom of biased / simplistic thinking.
In Wanting to Learn “everything”
To keep oneself up to date with related fields and being able to generate beautiful scientific illustrations are, I’d argue, nice professional skills to have. But the often-forgotten accounting on this issue concerns the opportunity costs.
In a given time frame, if there are things one would like to do more of, some others need to be sacrificed. The time sunk into “nice to have, but not essential” skills may be statistically expected to hinder, rather than aid one’s journey into the unknown.
To learn everything is not possible … to summarize it, blunt and obvious.
In being tech-driven
New techniques can be appealing, even sexy, but the fundamental property of research that I wish to pursue is not fashion. Rather, one should value continual and coherent consideration of deep questions.
A perhaps common (what can I say, I’ve only worked done 2.5 summer researches) example of this issue is the Bottom-Up narratives in a scientific project. Without a grasp on a main question to investigate (Top-Down logic), a Bottom-Up narrative runs the risk of quickly turning into a mush of disjoint technicalities.
IN KEEPing to the well-paved Track
Science might be about the contrary of this. So don’t.