Collecting Data = good. Realizing the data you collected was useless = bad. Lesson learned = priceless
I remember the first Friday in HH; I had already read through the blog post weekly assignments, and that Friday, when I realized all my solutions and data were useless. I thought to myself “Well, if nothing else, I have something to talk about for my week 5 blog post.” Then I worked superdiligently the next week and the following Monday (more than a week later) I realized everything I had done the second week was also useless. That was disheartening on account of the fact that I was coming off of a “Aw man, useless data” experience already, so I was ready to collect good data and be ready to go.
Moral of the story: good experiences and bad experiences don’t alternate nicely. You won’t know until you do your data analysis. Use that data analysis to not make the same mistakes next time.
It works; I promise. In the past two weeks I have collected awesome data. It has been VERY exciting. It has definitely been one of those “This is why I do research” type moments in terms of the fact that I prepared these solutions from scratch, mixed up a cocktail, ran it in the spectrophotometer and tadaa!!! GOOD NUMBERS! It’s really a great feeling. I’ve doubt you’ve experienced it testing solutions in a spec, but I’m sure you can relate to it in some fashion. When things work out in the lab, it’s always a pleasant surprise (yes, a SURPRISE). Another very important lesson I should also share was when I was coming off one of my “Argh. Bad data” experiences and I was talking to my mentor. We revamped the procedure, and then I got all excited and said something like “Oh, ok, so after I run x, y, z, stock solutions, and see the curve, then I can get started using the other protein?” She looked at me admonishingly and said, matter-of-factly, “Don’t be too optimistic about your data.”
Touché.
Most of what I do every day all day in the lab is data collection, which is why my happy and not so happy times are related to that. Other happy times include lab meetings when you ask lots of questions and when you realize the magnitude and scope of what you are doing in the greater scheme of things. That in itself is very exciting, but it is not phenomenal unless you end up collecting data to support it (remember, not too much optimism . . . just enough to motivate you).
The main moral of the story is that even when bad things happen (like realizing the solutions you were running for past two weeks were just dirty water), those are lessons that make your good times that much better. Not only by comparison, but also in that you learn exponentially more from mistakes and bad experiences than success and good ones, about yourself, general lab procedure, science, and most importantly: the research experience (I guess that's why most researchers have been researching for so long. They spent the first few years of their careers screwing up, but then started getting good data, and it was ADDICTING!).
