Not measuring twice before I cut (machining), directly after my adviser told me this.

Before that happened:

I'm not sure if I can count it as a mistake, since it's mostly his fault, but a month or so after I started my adviser came up to me and asked why I had been drawing all these plans, and not using the specs. No one told me that someone else had already designed the laser I supposed to be building.

Wasting time fixing a problem rather then starting a fresh i.e. biology/genetics - rather then just ordering in new primers(oligos), reagents(Taq, nucleotides etc) wasted a few weeks trying to find out which specific component was faulty. All the components are not very expensive while time tends to be - especially for scholarships.

Here are a few that I've seen happen time and time again in psychology labs - the main solutions are related to paying specific attention to data management issues that are not typically part of research psychology instruction (but should be).

Failing to keep redundant copies of original data. Even if automatic backup procedures are in place, it is important to make sure they are doing what you think they are doing.

Failing to document original data so that someone else can understand it. I don't know how many files I've come across with experimental conditions labelled "A,B,C" or similar, that can no longer be reconstructed. Including data generated by my past self, I'm sorry to say.

Failing to retain a clear and replicable sequence from original data to "results". If an error creeps in, much harder to figure out where it came from.

Not taking account of multiplicity.

Ideally you should plan exactly how you're going to analyse the data before the experiment but in reality students muddle through a bit.

analyzing data in multiple ways is a big no-no if you're just hunting for that elusive 0.05 P value to get it published.

It's stupid and causes statisticians to tear their hair out(both the arbitrary requirement a lot of journals set and the bad stats by researcher) but it's the reality in a lot of research.

Doing that can be compensated for as long as you keep track of what you tried and make that data available.

It's even worse because often people, including experienced professors, delude themselves with bad stats and waste time and money chasing statistical phantoms because they went significance mining.

Here's one thing from when I did undergraduate research:

I expected grad students to know what they are doing and practice safety. I worked in a lab that had a powerful UV light that we kept off to the side under a cardboard box. The light was used to accelerate curing of UV activated glue. The light was powerful enough to basically give you the equivalent of a sunburn to your eyes if you even glanced at it. When we used the light, we made sure to announce its use and cover it up before plugging it back in. One student in our lab moved the lamp to the middle of the room and turned it on as I was walking through the room right in front of my face. I said "I saw that" and he shook it off as if it were nothing. It was definitely not nothing. Within about 8 hours, I was essentially blind because my eyes were now much more sensitive to light. I spent about two days effectively blind. I don't recall discussing this with the professor I was working under, but it did help me decide to stop working for that professor. I should have discussed this with them. Thankfully, there was no lasting damage to my vision. At this point I use all PPE available, and am a stickler when it comes to safety. Worse, my work is now entirely computational!

Organic Chemistry lab --

Label everything, especially when two subsequent steps of your reaction look very similar.

If you're going to leave something stirring overnight, make sure there's a backup power supply, especially if your area has a history of power failures.

Not mine, but -- If the temperature of your oil bath seems to be going up much more slowly than usual, check to make sure the thermometer is working properly. Don't just turn the heat up until the temperature until the thermometer reads correctly. One of the people in my lab managed to cook his compound at 280 C because the tip of the thermometer was slightly above the surface of the oil bath.

CS grad student here. Some mistakes I made were

• Not documenting code.
• Naming figures/datafiles poorly, so that you have no idea what they are in two weeks. It's best to have an automated way of labeling files if you'll be creating a lot of them.
• Storing data in an inefficient way (very bad if you're generating large amounts of data).
• Not using version control.
• Diving right into implementing an algorithm without first thinking about whether that's the best way to solve the problem.
• Being intimidated by tasks that looked difficult (they were rarely as hard as I thought they would be).

Using idioms when talking to my advisor.

For example in my language there is an idiom "somebody broke the bank" which means that we've run out of something. So one day I told my advisor, that I can't start a new culture (some eucaryotic cells), because somebody broke the bank, meaning that there are no samples left in the big freezer. And she understood that somebody managed to literally break the liquid nitrogen cell bank. That was a loooong day for me.

Oh, and when your supervisor tell you to do something the fast way, by omitting some safety - don't do that. A friend of mine got poisoned with benzoic acid this way.

I had it right in the actual program where it mattered, but when I was giving my talk on it, I was asked about the time-step in a Bortz-Kalos-Lebowitz Monte Carlo simulation, and I said it was the inverse of the occurrence rate of the selected event instead of the inverse of the sum of the occurrence rates for all events.

To be more concrete - if there were 100 things that could happen, and each one would happen on average every 5 seconds, the BKL algorithm would pick one and then advance time by 5/100 seconds. I said it would advance time by 5 seconds.

For some reason, people took me at my word and concluded that the algorithm was prone to occasionally having nothing at all happen for extended periods of time (which is precisely what it's made not to do), not to mention scaling improperly with system size.

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.

I won't be able to respond individually to everyone, but thank you all for your contributions! If anything else comes to mind, please leave more quotes -- I'll check back periodically.

I once spilled lactic acid because I just assumed it flowed as fast as water. It would have been a simple mistake, but I specifically obtained that bottle from my twin sister who had prepared a similar mounting medium with it AND warned me to use it sparingly because buying it takes half a workday, ..., and I had listened and promised to be careful. I could have her show me how to do it, but she was time-pressed and irritable because her supervisor demanded she present her thesis before the institute (something like a preliminary hearing). Looking back now, I realize that asking her help then would have only taken five minutes, and there were literally no people who would be more willing and able to go out of their ways to do it.

I think asking yourself 'does it really cost so much to approach this person for advice?' is a good heuristic to have.

It's not quite what you are asking for, but speaking of mistakes in labs I recommend http://pipeline.corante.com/archives/things_i_wont_work_with/ :-)

http://www.buzzfeed.com/kmallikarjuna/how-to-science-as-told-by-17-overly-honest-scientists

I'm not sure how much of this is mistakes, exactly, but it's definitely about sub-optimal experiments. Sometimes way sub-optimal.

Drawback: there is no way to verify this material.