A metaphor: Knowledge is a jigsaw puzzle, and the search for truth is a process of trial and error fitting new pieces alongside those already have. The more pieces you have in place, the quicker you can accept or reject new ones; the more granular the detail you perceive in their edges, the better you can identify the exact shape of holes in the puzzle and make new discoveries.
And if there’s a misshapen piece you absolutely refuse to move it will screw up the entire puzzle and you’ll never get it right. This method is great—generally reliable sources which fit together are free pieces which act as your foundation to even get started.
Unfortunately, it’s often easy and natural to force contradicting new data into your existing model even if it really doesn’t fit—patching the conflicts without ever really noticing the dissonance, and overfitting your theory without actually restructuring your beliefs. One useful trick for checking yourself: explicitly asking yourself “what do I expect this figure / fact to be or say?” on each step of the project before you look it up. If you go in with reasonably certain expectations and the data reads wildly out of bounds, maybe you’ve found a major hole in your understanding of the issue, maybe the info is bad, or maybe that figure is saying something very different than you interpreted it.
A metaphor: Knowledge is a jigsaw puzzle, and the search for truth is a process of trial and error fitting new pieces alongside those already have. The more pieces you have in place, the quicker you can accept or reject new ones; the more granular the detail you perceive in their edges, the better you can identify the exact shape of holes in the puzzle and make new discoveries.
And if there’s a misshapen piece you absolutely refuse to move it will screw up the entire puzzle and you’ll never get it right. This method is great—generally reliable sources which fit together are free pieces which act as your foundation to even get started.
Unfortunately, it’s often easy and natural to force contradicting new data into your existing model even if it really doesn’t fit—patching the conflicts without ever really noticing the dissonance, and overfitting your theory without actually restructuring your beliefs. One useful trick for checking yourself: explicitly asking yourself “what do I expect this figure / fact to be or say?” on each step of the project before you look it up. If you go in with reasonably certain expectations and the data reads wildly out of bounds, maybe you’ve found a major hole in your understanding of the issue, maybe the info is bad, or maybe that figure is saying something very different than you interpreted it.