Why the simplest answer is often the smartest starting point

The Occam's Razor.
During my PhD, I worked on finding ways to detect diseases very early. I would use an imaging system built in the lab and then recruit patients, both healthy and diseased. The goal was to use this system to obtain images of the retina (the layer at the back of the eye), with very good contrast of the vessels and its surrounding structure.

Now, there were so many post processing algorithms available to us, but the easiest was always 80% effective. And that is obtaining repeated scans of the same location and averaging them pixel by pixel. What happens is the noise in the background of the image appears randomly, so it has a much lower average, whereas the vessel structure becomes more apparent with better contrast. We did not need anything elaborate for that.

This is what Occam's razor is about. When you have competing explanations or approaches, start with the simplest one. It does not mean the simplest answer is always right, but before you reach for something complex, ask yourself if the straightforward approach has been explored. Most of the time, it has not.

I think about this a lot outside of research. When something goes wrong, we tend to search for elaborate explanations when the reality is that you probably just did not follow up, or you were not clear enough, or you skipped a step. We overcomplicate things because simple answers do not feel satisfying enough.

The way I see it, the simpler your explanation, the fewer assumptions you are making. And fewer assumptions means you have less room to be wrong. I am not saying you should never go deeper, sometimes the complex answer is the right one. But start simple. If averaging repeated scans gets you 80% of the way there, that is not a bad place to begin.