A diverse basis for science

The sum of our experiences, environment, and genetic predispositions form the basis for how we view and interpret the world. This is a principle of many philosophies and helps to explain the broad diversity in human behavior.

The practical view of science is of a rigid structure built upon basic assumptions and prior knowledge. After the proper application of "the scientific method," this foundation leads us to new discoveries. Roughly speaking, the method goes as such: we start from our current state of knowledge, make a hypothesis followed by observations, formulate a proper model that accommodates the data, then draw our conclusions while taking into account the prior information. This outline must follow the rules of logic and not contradict what we already know to be true (and if it does, we place this contradiction under extreme scrutiny until the contradiction is resolved).

This generalization of the scientific method is confounded by the inherent variability in the assumptions from which it starts. In reality, every one possesses a different set of beliefs regarding scientific inquiry. This is exactly analogous to the variety of metaphysical beliefs held by people across the globe. And, just as this variety gives rise to the diversity of people, it leads scientists to different interpretations of theirs and others' work.

Rather than enforce a common basis for the pursuit of science, scientists ought to respect the basic diversity within their own field. Science is more than rote application of a formula; it engages the scientist to the point that discovery becomes an act of self-expression. A study is flavored with the thoughts and feelings of the people involved and can not be separated from them. Once this is understood, we can see that science is a very human endeavor and not the cold, calculated formula known as the scientific method.

Grad students != drones

A major difference I notice between graduate students and my friends who work in industry is that there is a significant sense of pride and ownership taken by the latter group with their work. This could be for a variety of reasons, including higher monetary compensation, the real-world potential of their work, and motivation provided by their employers.

On the other hand, a natural curiosity (apart from the promise of a degree) would ideally drive a graduate student to perform quality research, but I find that this is sometimes not the case. Many graduate students feel that they are forced into their projects solely because it furthers the career of their advisor. A student-advisor relationship grounded in this sentiment leads the student to do just enough work to graduate, but does not provide enough motivation for the student to fully realize the potential of their work. Some ownership in the project is needed.

How can a mutually beneficial environment be created within an academic setting? I think advisors should think hard about this issue since they stand to benefit greatly from an increase in the quality of their students' research. I don't think that the prospect of earning an advanced degree is enough to establish such an environment; graduate students need to see their research as something beyond a means to graduate if science is truly going to progress.

Some things that advisors do that remove the sense of ownership from their graduate students' work include

1. having their graduate students attempt numerous "impossible" experiments in the hope that one might actually work;
2. writing the journal papers on the projects themselves;
3. delaying the graduation of senior students for their experience in the lab;
4. and frequently deferring communication and guidance to post-docs.

Measurement efficiency

While I was helping one of the undergraduates in our group yesterday I started thinking about the efficiency of measurements and how we can avoid wasting time in the laboratory with poorly planned experiments.

By an efficient measurement I mean an experimental procedure that extracts the most information possible from the collected data with the least effort. The second part of my definition, expending the least effort, is usually common sense. After all, I require nothing more than a ruler to measure the length of something that is nearly the size of the ruler. Pulsed radar or laser interferometry are obviously too complicated for this task.

However, it is usually common sense because people erroneously think that more data is always better. If a certain phenomenon is known to be a linear function of some variable, for example, then I only need to take enough data points to assign statistically meaningful values to the best-fit line's slope and intercept. I've frequently seen my colleagues painstakingly collect so many data points as to make their plot appear continuous when in fact the curve describing the data was unimodal or uniformly increasing or decreasing. Much less effort could have been expended by reducing the number of measurements performed in these cases [1].

These examples also help illustrate the first part of my definition of an efficient measurement—extracting the most information possible. In the example about the data modeled by a line, there are only two pieces of relevant information: the slope and intercept. In fact, if it weren't for noise and measurement uncertainty, only two data points would be needed to maximize the amount of information gained. More complicated situations would likely involve performing measurements to increase my belief in a certain conclusion but may not outright prove that conclusion true. In these cases, an efficient measurement would optimize my belief based on the data it provides.

There is one subtle point to an information theoretic viewpoint of measurements that I've failed to discuss so far. The information that is extracted depends entirely upon the hypotheses being tested. That is, information is not physical. Measurements of voltage across a piece of material are only relevant if I want to know the material's electrical properties. So identifying exactly what I want to know about my system before I measure something about it is crucial in optimizing my measurement's efficiency.

In summary, an efficient measurement simplifies the means of data collection while maximizing the amount of information provided by the data. The information that a measurement provides is determined by the questions asked of the experimentalist; therefore, measurement efficiency is judged against these questions.

[1] Automated data acquisition has to a large extent made the number of data points collected irrelevant, but perhaps it has also caused many of us to neglect the question of efficiency in the first place.

Working hours do not correlate with productivity

A common topic in discussions I have with other graduate students concerns the proper amount of time that we should dedicate to our studies. The topic is relevant to helping us find the optimum work schedule, i.e. one that allows us to both find fulfillment with our studies and graduate in a timely manner. To simplify, let's say that the optimum work schedule maximizes our productivity.

Let's first begin by grouping graduate students into broad categories by their work habits. These categories are by no means mutually exclusive or exhaustive. I do however believe that a majority of graduate students can be placed within at least one of them.

1. The 9-to-5'er: This graduate student treats her research as a regular job. She often works for three or four hour chunks of time, takes a half hour lunch, and generally leaves her work in the office/lab. Five day work weeks are the norm. I believe that this is a somewhat rare work schedule for graduate students.
2. The 8-to-6'er: Pretty much the same as the previous category, except the longer time spent at school means that a graduate student following this work schedule will take more or longer breaks during the day. Some weekend work may also occur. Most students at CREOL fall into this category, myself included.
3. The Night Owl: These students usually don't get to school until 1:00 PM and work until the late hours of the night. They also tend to consume the most coffee.
4. The Stay-At-Home Grad Student: These students typically have advisors who frequently travel or are not present in the lab. They may also have projects requiring a lot of programming and simulation—work that's easily done at home (thank you Remote Desktop).
5. The Stay-At-School Grad Student: Hygiene and a social life are extraneous for these students. Perhaps a product of www.hulu.com and the digital media revolution, the stay-at-school graduate student finds no need to go home when TV can be piped directly to her computer.
6. The Random Worker: If there's work to do, the random worker will spend all her time, day and night, in the lab until it's done. Then she'll spend the next week at the beach. The random worker, like the stay-at-home graduate student, is usually a product of a hands-off advisor. They also tend to share the most in common with undergraduate work habits.

Based on my own observations, I don't believe that there is a strong correlation between the categories above and a grad student's overall optimal output. This is not an exciting conclusion, I know. But what is interesting is that these categories can also be assigned distinct (and, more importantly, different) values that relate to the average number of hours worked in a week. So, the interesting conclusion is that the hours worked has little-to-no effect on grad student productivity.

And don't worry. I have more than my own anecdotal evidence to support my claim. Two articles in Nature [here and here] reported on two separate research groups: one with a brutal schedule and one that strongly supported life outside the lab. Both are successful and well-respected amongst their peers. Furthermore, American researchers (and workers from all occupations) work notoriously more hours a year than Europeans. It might be argued that this does grant the Americans a technological edge, but I have my suspicions. Besides, Europeans are much happier.

I now wonder if the question of the proper amount of time spent working as a graduate student carries any real meaning. It presupposes that there exists some balance that's suitable for everyone, which is clearly not realistic.

And if this article is a bit incoherent, I apologize. I was too busy working this week to think it through thoroughly.

On blog writing (and happy 1.5'ish year anniversary!)

I've been writing on this blog for about a year and a half. As with any milestone (ok, so I missed the anniversary of the first post by six months), this fact has put me in a reflective mood and I'm eager to analyze what I've learned from this endeavor.

My original intent for starting the blog was to have a place to jot down my ideas and sort them according to some logical framework. My mind was a discombobulated mess of tasks I had to do, dates I had to keep, and theories that intermingled to the extent that I had difficulties keeping them straight. Of course, I could always jot down notes in a notebook or journal (and I do), but I found that my handwriting simply couldn't keep up with the rate at which my thoughts crystallized into a pattern in my mind. Thus, I started the blog, but honestly did not care whether others read it.

My first post sums up this intent very well. (Having written it so long ago, it actually seems as if someone else wrote it.) So has the blog succeeded in this regard? I think so. I've certainly managed to find a broad range of topics on which to flesh out my thoughts, and my thinking is much more clear than when I started.

The most amazing thing I've found from writing, especially through this medium, is that my intent for a post almost always changes as I write it, leading me to discover something in the process. For example, I first started this post as a discussion on writing techniques, inspired by this article. I suspect that the reason for this is that the public nature of a blog impresses upon me the need to be logical. I dare not commit a logical fallacy in front of the entire digital world, especially since I purport to be a "scientist." So, as I place my thoughts into writing, I'm better able to catch errors in my thinking, fix them, and as a result find new conclusions and ideas.

For this reason I've recently come to appreciate the value of an audience. Sure, my readership is next to nil, but building one takes work that I simply have not put in. That's changing now. Through the magic of Google, I've discovered ways to attract any sort of attention possible popularize my blog, including http://www.twitterfeed.com. If more people are reading what I write, I'll probably pay closer attention to it.

And what of the future? Do I have any more hopes for my writing? Well, trying to keep a regular post schedule has been challenging, so I hope that I can maintain it. I also hope that, if enough people do start reading this blog, that I can gain a lot from comments. I realize my viewpoint is very particular in a large field. My biases likely keep me from conclusions that are just as valid but I otherwise discount or miss entirely.

This was actually a rather easy and fun post to write. If you do enjoy this blog, feel free to share it with your friends who might find it interesting. I'd appreciate it ;)

On a side note: thank you Google for changing the layout of the editing screen on Blogger. The newer, more professional look means I don't have to Alt-Tab to Matlab when my advisor enters my office unexpectedly.

I'm not the only one with these suspicions...

“I have known more people whose lives have been ruined by getting a Ph.D. in physics than by drugs.” - Dr. Jonathan Katz

This quote, by way of this article, is a bit distressing. Still, the article reaffirms several thoughts I've had recently about the prospects of obtaining a job in academia. Academia is simply too saturated with workers. This article is a great summary of some recent data and opinion pieces and I think should be read by anyone who is planning to obtain their PhD in a scientific field in the near future.

http://promega.wordpress.com/2011/08/26/the-ph-d-glut-do-we-have-too-many-ph-d-s/

When is a conclusion good enough?

I've been doing a lot of reading recently on modern probability theory, Bayesian analysis, and information theory. One of the central tenets of these theories is that any proposition has associated with it a degree of plausibility, i.e. a probability of being true, that reflects the amount of information available. The proposition, "the sky is blue" is extremely plausible since it is based off of daily self-observations and confirmed by others. As another example, I judge the proposition, "thirty million Americans have blue eyes" as plausible based on the knowledge of the current population of the United States and my own observations on the frequency of encountering blue-eyed people. However, this is not as likely to be true as the first statement.

A conclusion in a scientific paper is nothing more than a proposition and thus possesses its own degree of plausibility. The information available to the reader for determining the plausibility of the conclusion is the data presented in the paper and all previously published work on the same topic. Of course, other factors weigh in, such as prejudices for particular theories and affinities or dislikes for the authors on the paper. Temporarily placing these other factors aside, I wonder, "when does a conclusion possess a large enough degree of plausibility to be considered true?"

Of course, a definite answer doesn't exist. No scientific proposition can be true with 100% certainty and it is silly to think that we can even assign a threshold probability for evaluating a scientific paper's correctness. Just imagine a paper successfully passing through the peer-review process so long as it is evaluated to be 78.63% or more true by its reviewers. But the question remains relevant. Science is a culture and every culture has criteria by which it evaluates claims.

In a closely-related post I wrote about the fallacies that workers commit while evaluating other work. But I find it much more difficult to identify the criteria for establishing the truthfulness and quality* of research. Unfortunately, I don't think I'll be able to fulfill my full potential as a scientist until I am capable of doing so.

Note: The problem of defining quality has been approached at great length by American author Robert Pirsig in his popular novel "Zen and the Art of Motorcycle Maintenance." One of his primary arguments is that Quality is actually an undefined construct present at the seminal moment when an observation is made and processed by the brain. People may know if something possesses Quality, but it is inherently undefinable.