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.