What is Science? Putting the ‘Philosophy’ back in Ph.D.

In the first blog post,  I gave an overview of the definition of style and how this concept relates to how we should think about doing science. Now after some philosophical discussions as of late with colleagues and after a morning spent pondering over this article by 538 about p-values, retractions, and how science is a lot harder than we usually give it credit for, I wanted to take the time to go back to giving a definition to the first part of 'Science with Style'.

We should not only think about and define the components of 'Science with Style' but we should also think about what getting a Ph.D. really means. Is it a degree of ‘Phinally done’ (as my UF alumni bumper sticker says) or ‘Phucking Do it’ (as your advisor might say) or ‘Piled higher and Deeper’ (as Jorge Cham says)? No, because the Ph stands for ‘philosophy’, and earning a Ph.D. means you shouldn’t just be an expert at pipetting or counting cells, you should be an expert in being  a scientist .

So what does the dictionary say about science? 
Science, noun: ‘The intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.’

Breaking down this definition piece by piece, we can see that science is meant to be more than just doing science. We can spend all our time in the lab generating data the rest of our lives and endless hours pouring over endless spreadsheets of results, playing with variables and looking for every possible combination of factors to tell us the meaning of the universe. As graduate students and post-docs especially, we spend a lot of our time as scientists-in-training doing science, but is that the same as being a scientist?

Part 1: ‘The intellectual and practical activity’
Graduate students and post-docs likely understand all too well the ‘practical activities’ that go into science. Although perhaps it’s a different issue of whether you want to call trudging to the lab at midnight on Christmas Eve to collect 12-hour time point samples or renting a 4X4 so you can drive to the middle of bear country to collect water samples ‘practical’ or not. In the early parts of our careers as scientists, our lives are dominated by the practical activities: rats to dissect, seeds to count, cells to split, livers to grind, surveys to conduct, field sites to map, machines to fix, data to normalize/analyze/synthesize/everything-ize. To non-scientists, and to many of us scientists that got interested in exploring the natural world at a young age, this is what science looks like, the clips they show on TV and movies of scientists making genetically engineered dinosaurs or solving crimes with fingernail clippings.

But the problem is that this is only half of what science is defined as, and only half of what science actually is. Especially as young scientists, busy with all of the practical tasks required to graduate or publish or get out of the lab by 9pm, it’s easy to forget about the intellectual side of science. There’s a reason for those 12-hour time points and water samples from bear country: you’re setting out to answer a key scientific question in order to address a specific hypothesis. Most of the time, especially in graduate school, this question and hypothesis wasn’t first crafted by you, but rather is part of a large grant your PI received or is related to some data that a previous student/post-doc collected 5 years ago in your lab. You’re expected as students to know the reason why you’re doing what you’re doing, but for most students you weren’t a part of the brainstorming and data analysis sessions that went into crafting your project. At the same time your PI is likely balancing other students’ projects and thinking ahead to other grants and collaborations, and may at some point have forgotten that afternoon sitting in their office when they came up with the brilliant idea that is your project.

This doesn’t mean that you’re doomed to some irrelevant project from years-old data or some idea your PI came up with in a caffeine-fueled brainstorming session that they happened to end up getting money for. As a scientist-in-training, your training includes both the intellectual and the practical sides of science, so you should focus on both of them. Do what you need to do in the lab, but before you run off to collect that 12-hour time point, go back to the beginning and think about the greater why of that time point:

-          Go back to the literature related to your project. And don’t just read the papers, think about how they did the experiments, the statistics and conclusions that came out of them, and whether what they say they found is what they actually found. Read not just for facts but to synthesize what’s been done before and how it all connects.
-          Approach everything you see with your own logic and let yourself see the data without the author’s or your PI's interpretation. Look at everything at a critical angle before you accept it as a (potential) truth. Science was made for cynics, not optimists, so take everything you find with a grain of salt!
-          After you get a handle on the literature, do an afternoon brainstorming session of your own. Where are the gaps in knowledge? What was observed that couldn’t be explained? Where is there a question that’s been left unanswered?

This may not look like science to you or to most people who think of TV and movie science, but this is where the difference between doing science and being a scientist lies. And how we go about being a scientists and answering these unanswered questions lies within the scientific method.

Part 2: The ‘systematic study’
At some point when your primary school teacher was getting your class ready to come up with a science fair project, you probably had a diagram similar to this hanging up somewhere in your classroom. This one comes from a science fair project website  and just because it was made for 13-year olds doesn’t mean you shouldn’t print it off immediately and hang it in your office:

At some point the scientific method was probably covered again during your undergraduate studies, maybe even in more than one of your courses. This all likely seemed too easy and common sense when you were 13, and your brain was soon filled with more important details like chemical reactions and math equations and the entire Krebs cycle. The issue is when we get so bogged down with the practical parts of science that we forget about the common sense/intellectual parts of science. It’s easy to get lost in the details and the experiments that you have to run to get data, but if you don’t understand why you’re doing what you’re doing, you’ll end up flailing away in the lab running a thousand different assays without any clue as to where the meaningful answers are.

The scientific method may be common knowledge when you look at it, even 13-year olds can get the gist of it, but we can’t push it aside or think that the details are the only thing that matter. The scientific method is the heart and the core of science as a field of study. It’s what makes science science, a field of study where we are trying to figure out ‘the structure and behavior of the physical and natural world. ’ We do this not just by banging our heads against the wall or coming up with things out of thin air, but with a systematic study that we follow for every single thing that we do, if we are to call ourselves scientists. But when bogged down with the details and practicalities of science, where do we start in order to make progress towards making sense of the world? We go back to the beginning, once again: We ask a question.

Part 3: ‘Observation and experiment’
Look back at the start of the scientific method diagram. Where does it begin? With a question about why you’ve seen something. In the case of your work, what you ‘see’ is what the literature in your field has told you already. What did someone find but couldn’t explain with what was already known? That’s what you’re setting out to do: to explain something that’s currently unknown. The observation part is what we do to understand what’s known and to help us ask a good question. The experiment is what we do to understand what’s not known and the work you do will lead you to an answer. And it’s not a good answer or a bad answer, it’s just an answer to your specific question.

While you can debate on the relevance of 1100+ significant p-values in 538’s article (perhaps there should be a multiple testing correction added into the widget), the take-home message in regards to science is this: The key part of science isn’t in finding good answers, but in asking good questions. You can play around with variables and experimental designs and will likely find different results every time. There’s a million ways to find an answer, and changes in policies and ideas about science are indicative of this: eggs are good, eggs are bad, don’t eat carbs after midnight, carbs don’t matter, Pluto is a planet, no it’s not. 

But what’s at the crux of science isn’t the answers, it’s the questions. And when you ask good questions, the answers you get back (regardless of what they are) are the meaningful ones that withstand the test of time and replication.  So whether you just desperately want to be 'Phinally Done' or are setting out to 'Phucking Do it' or feel like you're always 'Piled higher and Deeper', remember that what you're towards is becoming a Doctor in Philosophy, and that a Ph.D. is not just about doing good science but in becoming a great scientist.