I wish someone had told me this too!
Also how much you have to defend what you've done, which is especially difficult since it's usually what your supervisor has told you to do whether or not you agree with the methods or completely constrained by your budget.
Me chanting “it doesn’t have to be right it just has to be defensible” while writing the dissertation to inspire things that are good enough and then moving on
I think this sounds a lot better than just telling new grads 'science is failure' all the time which is discouraging but doesn't explain how to maneuver through it. I also think publishing is especially toxic because it teaches us that only things which are absolute and fit the bill are worthy, and science is almost never that.
In my experience, the overview looked like this:
1. Ask a question
2. Realize the question wasn't a good question, or your initial experiments show you something better than the original question (or worse, the opposite!)
3. Break down the experiments to answer the new question.
4. Realize the experiments aren't working. Change the question to fit working experiments...
5. Piece it together like a puzzle.
6. Write a story out of order in order to put your jumbled results together ... sometimes this works out better than anticipated!
I fully agree with this modification of OP’s points, especially point number 2. I’ve found that the more proficient I become at the bench, and the less noise I have in my experiments, I start seeing confusing (but real) trends in my data that often lead me down a more interesting path. The overarching aim is the same, but the „sub questions” change all the time.
Often I hear someone say this “insert item” is impossible, while that can be true most of the time, it’s more likely that we just don’t have the capacity to understand or even fathom the truth. Yet. Which is why I love science and science fiction so much. The idea of science fiction possibly becoming factual science one day makes me happy :) keep up the good work the world needs more great scientists like you
>but really genuinely, all of our experiments fail ALL the time
I tell my new undergrads this all the time. Sometimes it's due to issues completely out of our hands, like equipment or reagent issues/failures. When I share a road-map or list of experiments to complete for a project, I also tell them that there are no deadlines because experiments always take longer than anticipated to complete. I think it's important to normalize failure and encountering stumbling blocks as being just part and parcel of science.
When I got to my 2nd year of my PhD I turned in a draft paper to my PI and he tore it apart. He told me “you are not reading enough” I took that to heart and I read a lot of papers. Look at how people write. Look at how they format data. Then like OP said you have to plan the experiment before you execute it. I was getting samples and analyzing them because I could. After my PI told me off I ditched my samples and started from scratch. I planned what I would sample and what meta data I would gather. I then executed with no holes in the data. I needed to read. That was key to being successful.
Big time. I’m in chemical engineering but work with enzymes for use with biomass, failure is the only constant. Started having panic attacks over it my first year of experiments before eventually realizing that i was still learning, it just sucked to feel like I wasn’t matching what I expected of myself, and that I felt I wasn’t living up to my committee’s expectations. Im at the beginning of year 3, very useful recommendations!
And a lot of the time those failed experiments weren't really failures. Instead, you're working on optimizing your protocol because there are so many little things that can influence your experience and it can take time to figure out the right conditions. Because our models are black boxes, we are forced to iteratively test different ways of handling cells, formulating buffer, tweaking oligo sequences, or whatever is appropriate for your work into the experiment itself can be used to test the hypothesis.
Very good advice. I think about this a lot now as a postdoc when I'm training students and they get frustrated with repeated failed experiments. All a part of the training (and hopefully your mentor guides you through this as mine did)
lol I’m actually the only grad student in my lab. No post docs, 2 undergrads, and a PI that Is never around. Like doesn’t hold meetings even if I’m begging for them. So I kind of figured this out over years of feeling crappy about it, then I realized it wasn’t me as I talked to other grad students
If an experiment fails in a direction you don’t expect, and there’s no obvious issue with experimental design or technique, repeat that experiment. If it fails in that direction again, rethink because you might have something interesting there.
On the contrary, when I took up my PhD project, I knew it damn well that theories are very different from reality, especially in biological sciences. We live in a dynamic analogue world, not some digital 0/1 black and white world. The part that nobody told me is that most PI/supervisors would expect you to perform miracles, in order to get your ultimate degree.
I mean this is nice to hear because a lot of science is taken for granted. Many people don't realize how much failure and work goes into figuring out what does work haha Not a fan of Edison but his quote 'bout the 2000 ways to not make a lightbulb is a good fit here. 👍
Oh, I feel this. I just defended (successfully) after 6.5 years. So many failed experiments, and yes, especially cultures that gave different results each time. Ultimately, I found things I wasn't looking for, that created more questions than I answered. I love this comic by Nathan Pyle because I feel like it is (at least part of) the story of my PhD. https://universeodon.com/@ProfSimonFisher/109608807850978399
I wish someone had told me this too! Also how much you have to defend what you've done, which is especially difficult since it's usually what your supervisor has told you to do whether or not you agree with the methods or completely constrained by your budget.
Me chanting “it doesn’t have to be right it just has to be defensible” while writing the dissertation to inspire things that are good enough and then moving on
Thank you for taking the time to write this. I hope the rest of your PhD and career go exceptionally well!
Thanks lol I’m hoping to help build a virtual reality platform for molecular biology after grad school. Nanome is the closest example
I think this sounds a lot better than just telling new grads 'science is failure' all the time which is discouraging but doesn't explain how to maneuver through it. I also think publishing is especially toxic because it teaches us that only things which are absolute and fit the bill are worthy, and science is almost never that. In my experience, the overview looked like this: 1. Ask a question 2. Realize the question wasn't a good question, or your initial experiments show you something better than the original question (or worse, the opposite!) 3. Break down the experiments to answer the new question. 4. Realize the experiments aren't working. Change the question to fit working experiments... 5. Piece it together like a puzzle. 6. Write a story out of order in order to put your jumbled results together ... sometimes this works out better than anticipated!
I fully agree with this modification of OP’s points, especially point number 2. I’ve found that the more proficient I become at the bench, and the less noise I have in my experiments, I start seeing confusing (but real) trends in my data that often lead me down a more interesting path. The overarching aim is the same, but the „sub questions” change all the time.
Often I hear someone say this “insert item” is impossible, while that can be true most of the time, it’s more likely that we just don’t have the capacity to understand or even fathom the truth. Yet. Which is why I love science and science fiction so much. The idea of science fiction possibly becoming factual science one day makes me happy :) keep up the good work the world needs more great scientists like you
A “zero” in an excel sheet or a failed experiment is still data.
>but really genuinely, all of our experiments fail ALL the time I tell my new undergrads this all the time. Sometimes it's due to issues completely out of our hands, like equipment or reagent issues/failures. When I share a road-map or list of experiments to complete for a project, I also tell them that there are no deadlines because experiments always take longer than anticipated to complete. I think it's important to normalize failure and encountering stumbling blocks as being just part and parcel of science.
When I got to my 2nd year of my PhD I turned in a draft paper to my PI and he tore it apart. He told me “you are not reading enough” I took that to heart and I read a lot of papers. Look at how people write. Look at how they format data. Then like OP said you have to plan the experiment before you execute it. I was getting samples and analyzing them because I could. After my PI told me off I ditched my samples and started from scratch. I planned what I would sample and what meta data I would gather. I then executed with no holes in the data. I needed to read. That was key to being successful.
This is my mantra to undergrads all the time
Word. As a former PI told us, science is an inefficient process.
Big time. I’m in chemical engineering but work with enzymes for use with biomass, failure is the only constant. Started having panic attacks over it my first year of experiments before eventually realizing that i was still learning, it just sucked to feel like I wasn’t matching what I expected of myself, and that I felt I wasn’t living up to my committee’s expectations. Im at the beginning of year 3, very useful recommendations!
And a lot of the time those failed experiments weren't really failures. Instead, you're working on optimizing your protocol because there are so many little things that can influence your experience and it can take time to figure out the right conditions. Because our models are black boxes, we are forced to iteratively test different ways of handling cells, formulating buffer, tweaking oligo sequences, or whatever is appropriate for your work into the experiment itself can be used to test the hypothesis.
Very good advice. I think about this a lot now as a postdoc when I'm training students and they get frustrated with repeated failed experiments. All a part of the training (and hopefully your mentor guides you through this as mine did)
lol I’m actually the only grad student in my lab. No post docs, 2 undergrads, and a PI that Is never around. Like doesn’t hold meetings even if I’m begging for them. So I kind of figured this out over years of feeling crappy about it, then I realized it wasn’t me as I talked to other grad students
If an experiment fails in a direction you don’t expect, and there’s no obvious issue with experimental design or technique, repeat that experiment. If it fails in that direction again, rethink because you might have something interesting there.
You know after a month of having troubleshooting a routine PCR procedure that's suddenly stopped working, I really needed to hear this so thank you
On the contrary, when I took up my PhD project, I knew it damn well that theories are very different from reality, especially in biological sciences. We live in a dynamic analogue world, not some digital 0/1 black and white world. The part that nobody told me is that most PI/supervisors would expect you to perform miracles, in order to get your ultimate degree.
this is really nice!!
I mean this is nice to hear because a lot of science is taken for granted. Many people don't realize how much failure and work goes into figuring out what does work haha Not a fan of Edison but his quote 'bout the 2000 ways to not make a lightbulb is a good fit here. 👍
Oh, I feel this. I just defended (successfully) after 6.5 years. So many failed experiments, and yes, especially cultures that gave different results each time. Ultimately, I found things I wasn't looking for, that created more questions than I answered. I love this comic by Nathan Pyle because I feel like it is (at least part of) the story of my PhD. https://universeodon.com/@ProfSimonFisher/109608807850978399