Hello, Less Wrong!
This seems like a community with a relatively high density of people who have worked in labs, so I'm posting here.
I recently finished the first draft of something I'm calling "The Hapless Undergraduate's Guide to Research" (HUGR). (Yes, "HUGS" would be a good acronym, but "science" isn't specific enough.) Not sure if it will ever be released, or what the final format will be, but I'll need more things to put in it whatever happens.
Basically, this is meant to be an ever-growing collection of mistakes that new researchers (grad or undergrad) have made while working in labs. Hundreds of thousands of students around the English-speaking world do lab work, and based on my own experiences in a neuroscience lab, it seems like things can easily go wrong, especially when rookie researchers are involved. There's nothing wrong with making mistakes, but it would be nice to have a source of information around that people (especially students) might read, and which might help them watch out for some of the problems with the biggest pain-to-ease-of-avoidance ratios.
Since my experience is specifically in neuroscience, and even more specifically in "phone screening and research and data entry", I'd like to draw from a broad collection of perspectives. And, come to think of it, there's no reason to limit this to research assistants--all scientists, from CS to anthropology, are welcome!
So--what are some science mistakes you have made? What should you have done to prevent them, in terms of "simple habits/heuristics other people can apply"? Feel free to mention mistakes from other people that you've seen, as long as you're not naming names in a damaging way. Thanks for any help you can provide!
And here are a couple of examples of mistakes I've gathered so far:
--Research done with elderly subjects. On a snowy day, the sidewalk froze, so subjects couldn't be screened for a day, because no one thought to salt the sidewalks in advance. Lots of scheduling chaos.
--Data entry being done for papers with certain characteristics. Research assistants and principal investigator were not on the same page regarding which data was worth collecting. Each paper had to be read 7 or 8 times by the time all was said and done, and constructing the database took six extra weeks.
--A research assistant clamped a special glass tube too tight, broke it, and found that replacements would take weeks to come in... well, there may not be much of a lesson in that, but maybe knowing equipment is hard to replace cold subconsciously induce more caring.
Advice from a math grad student.
Before you invest substantial time in a research project, make triply sure nobody has solved the problem already. Often there's an easy but time-consuming way to check this: if there's a paper that such a result is guaranteed to cite (such as the paper in which the problem was first posed), then read the abstracts of all 100+ papers citing it on Google Scholar or something. If there are a few such guaranteed citations, do this for all of them. If something looks vaguely related (most of the papers won't be), then skim through the whole paper.
I've been bitten by this 1.5 times. The first time around was my very first research project in graduate school; I assumed that the professor posing it had already performed this check. Once we had actually proven a result, we discovered that the same result was proven 14 years ago. Moreover, a few years later, the same author had solved the extension we were going to pursue next.
The second time around, I read a Wikipedia article which stated that no improvements to an upper bound were found. This was for semi-legitimate reasons because it required putting together two results in a trivial way to see that an upper bound already exists. By the time I realized this, we were already on the writing-a-paper stage; fortunately, our upper bound was better than the existing one, so we were saved.