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Not Omniscient

By Daniel Hubbard | January 19, 2014

Last week after a presentation one of the audience members came up to the front to say hello. She said that after hearing my introduction she had to tell me that she had been a physicist in the 1950s and that she understood the connection between being a physicist and being a genealogist. She added that in her experience, people didn’t really get that. I thanked her and agreed that I always needed to explain that transition. (I also made a mental note that I was in the presence of someone who had broken through a glass ceiling that was certainly much stronger then than it is now—very cool).

My normal explanation of the transition revolves around how it takes time to develop the mindset that research requires. Turning data every which way and really analyzing it does not come naturally either and must be learned. Learning that everything exists within a context and that framework needs to be understood before new data and new hypothesis can be processed is another task. Many years of physics does that.

The Opposite of Omniscience*

Today as I wrote a client report, I found myself suddenly hitting the back arrow to add a few words without really thinking about why. It was a reflex that had taken over. I’ve hit the back arrow for the same reason before but this time I stopped to think about why. I had written something along the lines of “there is no evidence,” then moved back with the cursor to the point between “no” and “evidence” and added the word “known.” Sometimes the phrase “no evidence” can be appropriate. It was not in what I was typing. The research was still very sketchy, the main characters very hazy. The fact that I needed to express was not that evidence does not exist in an absolute sense. Rather the fact is that nothing has yet been found. That only might indicate that there is nothing to find. It can be an important distinction. A researcher can know enough to consider something proven or disproven but the researcher is never omniscient. A researcher must state not just what has been done and what has been learned but also the possibilities for error and the sources of uncertainty. In my years as a physicist, much time was spent deriving error margins for data and calculations. It is not the most exciting of tasks but it is a very important one. Do it enough and it becomes a life lesson.

*When I started to think about writing this post, I tried to think of a good antonym for “omniscient.” The more I thought, the more I wondered if there is one. My thesaurus wasn’t much help. Google turned up some interesting discussions of what the antonym might be. Many suggestions were along the line of “ignorant.” Omniscient though, is an absolute. Its opposite might be the opposing absolute but it might also be anything in between, anything not absolute. Research can start with near ignorance and progress to knowledge but never to omniscience. Research works its way from one type of antonym to the other.

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