Category Archives: funding

whether or not you should pursue a career in science still depends mostly on that thing that is you

I took the plunge a couple of days ago and answered my first question on Quora. Since Brad Voytek won’t shut up about how great Quora is, I figured I should give it a whirl. So far, Brad is not wrong.

The question in question is: “How much do you agree with Johnathan Katz’s advice on (not) choosing science as a career? Or how realistic is it today (the article was written in 1999)?” The Katz piece referred to is here. The gist of it should be familiar to many academics; the argument boils down to the observation that relatively few people who start graduate programs in science actually end up with permanent research positions, and even then, the need to obtain funding often crowds out the time one has to do actual science. Katz’s advice is basically: don’t pursue a career in science. It’s not an optimistic piece.

My answer is, I think, somewhat more optimistic. Here’s the full text:

The real question is what you think it means to be a scientist. Science differs from many other professions in that the typical process of training as a scientist–i.e., getting a Ph.D. in a scientific field from a major research university–doesn’t guarantee you a position among the ranks of the people who are training you. In fact, it doesn’t come close to guaranteeing it; the proportion of PhD graduates in science who go on to obtain tenure-track positions at research-intensive universities is very small–around 10% in most recent estimates. So there is a very real sense in which modern academic science is a bit of a pyramid scheme: there are a relatively small number of people at the top, and a lot of people on the rungs below laboring to get up to the top–most of whom will, by definition, fail to get there.

If you equate a career in science solely with a tenure-track position at a major research university, and are considering the prospect of a Ph.D. in science solely as an investment intended to secure that kind of position, then Katz’s conclusion is difficult to escape. He is, in most respects, correct: in most biomedical, social, and natural science fields, science is now an extremely competitive enterprise. Not everyone makes it through the PhD; of those who do, not everyone makes it into–and then through–one more more postdocs; and of those who do that, relatively few secure tenure-track positions. Then, of those few “lucky” ones, some will fail to get tenure, and many others will find themselves spending much or most of their time writing grants and managing people instead of actually doing science. So from that perspective, Katz is probably right: if what you mean when you say you want to become a scientist is that you want to run your own lab at a major research university, then your odds of achieving that at the outset are probably not very good (though, to be clear, they’re still undoubtedly better than your odds of becoming a successful artist, musician, or professional athlete). Unless you have really, really good reasons to think that you’re particularly brilliant, hard-working, and creative (note: undergraduate grades, casual feedback from family and friends, and your own internal gut sense do not qualify as really, really good reasons), you probably should not pursue a career in science.

But that’s only true given a rather narrow conception where your pursuit of a scientific career is motivated entirely by the end goal rather than by the process, and where failure is anything other than ending up with a permanent tenure-track position. By contrast, if what you’re really after is an environment in which you can pursue interesting questions in a rigorous way, surrounded by brilliant minds who share your interests, and with more freedom than you might find at a typical 9 to 5 job, the dream of being a scientist is certainly still alive, and is worth pursuing. The trivial demonstration of this is that if you’re one of the many people who actuallyenjoy the graduate school environment (yes, they do exist!), it may not even matter to you that much whether or not you have a good shot of getting a tenure-track position when you graduate.

To see this, imagine that you’ve just graduated with an undergraduate degree in science, and someone offers you a choice between two positions for the next six years. One position is (relatively) financially secure, but involves rather boring work of quesitonable utility to society, an inflexible schedule, and colleagues who are mostly only there for a paycheck. The other position has terrible pay, but offers fascinating and potentially important work, a flexible lifestyle, and colleagues who are there because they share your interests and want to do scientific research.

Admittedly, real-world choices are rarely this stark. Many non-academic jobs offer many of the same perceived benefits of academia (e.g., many tech jobs offer excellent working conditions, flexible schedules, and important work). Conversely, many academic environments don’t quite live up to the ideal of a place where you can go to pursue your intellectual passion unfettered by the annoyances of “real” jobs–there’s often just as much in the way of political intrigue, personality dysfunction, and menial due-paying duties. But to a first approximation, this is basically the choice you have when considering whether to go to graduate school in science or pursue some other career: you’re trading financial security and a fixed 40-hour work week against intellectual engagement and a flexible lifestyle. And the point to note is that, even if we completely ignore what happens after the six years of grad school are up, there is clearly a non-negligible segment of the population who would quite happy opt for the second choice–even recognizing full well that at the end of six years they may have to leave and move onto something else, with little to show for their effort. (Of course, in reality we don’t need to ignore what happens after six years, because many PhDs who don’t get tenure-track positions find rewarding careers in other fields–many of them scientific in nature. And, even though it may not be a great economic investment, having a Ph.D. in science is a great thing to be able to put on one’s resume when applying for a very broad range of non-academic positions.)

The bottom line is that whether or not you should pursue a career in science has as much or more to do with your goals and personality as it does with the current environment within or outside of (academic) science. In an ideal world (which is certainly what the 1970s as described by Katz sound like, though I wasn’t around then), it wouldn’t matter: if you had any inkling that you wanted to do science for a living, you would simply go to grad school in science, and everything would probably work itself out. But given real-world constraints, it’s absolutely essentially that you think very carefully about what kind of environment makes you happy and what your expectations and goals for the future are. You have to ask yourself: Am I the kind of person who values intellectual freedom more than financial security? Do I really love the process of actually doing science–not some idealized movie version of it, but the actual messy process–enough to warrant investing a huge amount of my time and energy over the next few years? Can I deal with perpetual uncertainty about my future? And ultimately, would I be okay doing something that I really enjoy for six years if at the end of that time I have to walk away and do something very different?

If the answer to all of these questions is yes–and for many people it is!–then pursuing a career in science is still a very good thing to do (and hey, you can always quit early if you don’t like it–then you’ve lost very little time!). If the answer to any of them is no, then Katz may be right. A prospective career in science may or may not be for you, but at the very least, you should carefully consider alternative prospects. There’s absolutely no shame in going either route; the important thing is just to make an honest decision that takes the facts as they are and not as you wish that they were.

A couple of other thoughts I’ll add belatedly:

  • Calling academia a pyramid scheme is admittedly a bit hyperbolic. It’s true that the personnel structure in academia broadly has the shape of a pyramid, but that’s true of most organizations in most other domains too. Pyramid schemes are typically built on promises and lies that (almost by definition) can’t be realized, and I don’t think many people who enter a Ph.D. program in science can claim with a straight face that they were guaranteed a permanent research position at the end of the road (or that it’s impossible to get such a position). As I suggested in this post, it’s much more likely that everyone involved is simply guilty of minor (self-)deception: faculty don’t go out of their way to tell prospective students what the odds are of actually getting a tenure-track position, and prospective grad students don’t work very hard to find out the painful truth, or to tell faculty what their real intentions are after they graduate. And it may actually be better for everyone that way.
  • Just in case it’s not clear from the above, I’m not in any way condoning the historically low levels of science funding, or the fact that very few science PhDs go on to careers in academic research. I would love for NIH and NSF budgets (or whatever your local agency is) to grow substantially–and for everyone get exactly the kind of job they want, academic or not. But that’s not the world we live in, so we may as well be pragmatic about it and try to identify the conditions under which it does or doesn’t make sense to pursue a career in science right now.
  • I briefly mention this above, but it’s probably worth stressing that there are many jobs outside of academia that still allow one to do scientific research, albeit typically with less freedom (but often for better hours and pay). In particular, the market for data scientists is booming right now, and many of the hires are coming directly from academia. One lesson to take away from this is: if you’re in a science Ph.D. program right now, you should really spend as much time as you can building up your quantitative and technical skills, because they could very well be the difference between a job that involves scientific research and one that doesn’t in the event you leave academia. And those skills will still serve you well in your research career even if you end up staying in academia.

 

Big Pitch or Big Lottery? The unenviable task of evaluating the grant review system

This week’s issue of Science has an interesting article on The Big Pitch–a pilot NSF initiative to determine whether anonymizing proposals and dramatically cutting down their length (from 15 pages to 2) has a substantial impact on the results of the review process. The answer appears to be an unequivocal yes. From the article:

What happens is a lot, according to the first two rounds of the Big Pitch. NSF’s grant reviewers who evaluated short, anonymized proposals picked a largely different set of projects to fund compared with those chosen by reviewers presented with standard, full-length versions of the same proposals.

Not surprisingly, the researchers who did well under the abbreviated format are pretty pleased:

Shirley Taylor, an awardee during the evolution round of the Big Pitch, says a comparison of the reviews she got on the two versions of her proposal convinced her that anonymity had worked in her favor. An associate professor of microbiology at Virginia Commonwealth University in Richmond, Taylor had failed twice to win funding from the National Institutes of Health to study the role of an enzyme in modifying mitochondrial DNA.

Both times, she says, reviewers questioned the validity of her preliminary results because she had few publications to her credit. Some reviews of her full proposal to NSF expressed the same concern. Without a biographical sketch, Taylor says, reviewers of the anonymous proposal could “focus on the novelty of the science, and this is what allowed my proposal to be funded.”

Broadly speaking, there are two ways to interpret the divergent results of the standard and abbreviated review. The charitable interpretation is that the change in format is, in fact, beneficial, inasmuch as it eliminates prior reputation as one source of bias and forces reviewers to focus on the big picture rather than on small methodological details. Of course, as Prof-Like Substance points out in an excellent post, one could mount a pretty reasonable argument that this isn’t necessarily a good thing. After all, a scientist’s past publication record is likely to be a good predictor of their future success, so it’s not clear that proposals should be anonymous when large amounts of money are on the line (and there are other ways to counteract the bias against newbies–e.g., NIH’s approach of explicitly giving New Investigators a payline boost until they get their first R01). And similarly, some scientists might be good at coming up with big ideas that sound plausible at first blush and not so good at actually carrying out the research program required to bring those big ideas to fruition. Still, at the very least, if we’re being charitable, The Big Pitch certainly does seem like a very different kind of approach to review.

The less charitable interpretation is that the reason the ratings of the standard and abbreviated proposals showed very little correlation is that the latter approach is just fundamentally unreliable. If you suppose that it’s just not possible to reliably distinguish a very good proposal from a somewhat good one on the basis of just 2 pages, it makes perfect sense that 2-page and 15-page proposal ratings don’t correlate much–since you’re basically selecting at random in the 2-page case. Understandably, researchers who happen to fare well under the 2-page format are unlikely to see it that way; they’ll probably come up with many plausible-sounding reasons why a shorter format just makes more sense (just like most researchers who tend to do well with the 15-page format probably think it’s the only sensible way for NSF to conduct its business). We humans are all very good at finding self-serving rationalizations for things, after all.

Personally I don’t have very strong feelings about the substantive merits of short versus long-format review–though I guess I do find it hard to believe that 2-page proposals could be ranked very reliably given that some very strange things seem to happen with alarming frequency even with 12- and 15-page proposals. But it’s an empirical question, and I’d love to see relevant data. In principle, the NSF could have obtained that data by having two parallel review panels rate all of the 2-page proposals (or even 4 panels, since one would also like to know how reliable the normal review process is). That would allow the agency to directly quantify the reliability of the ratings by looking at their cross-panel consistency. Absent that kind of data, it’s very hard to know whether the results Science reports on are different because 2-page review emphasizes different (but important) things, or because a rating process based on an extended 2-page abstract just amounts to a glorified lottery.

Alternatively, and perhaps more pragmatically, NSF could just wait a few years to see how the projects funded under the pilot program turn out (and I’m guessing this is part of their plan). I.e., do the researchers who do well under the 2-page format end producing science as good as (or better than) the researchers who do well under the current system? This sounds like a reasonable approach in principle, but the major problem is that we’re only talking about a total of ~25 funded proposals (across two different review panels), so it’s unclear that there will be enough data to draw any firm conclusions. Certainly many scientists (including me) are likely to feel a bit uneasy at the thought that NSF might end up making major decisions about how to allocate billions of dollars on the basis of two dozen grants.

Anyway, skepticism aside, this isn’t really meant as a criticism of NSF so much as an acknowledgment of the fact that the problem in question is a really, really difficult one. The task of continually evaluating and improving the grant review process is not one anyone should want to take on lightly. If time and money were no object, every proposed change (like dramatically shortened proposals) would be extensively tested on a large scale and directly compared to the current approach before being implemented. Unfortunately, flying thousands of scientists to Washington D.C. is a very expensive business (to say nothing of all the surrounding costs), and I imagine that testing out a substantively different kind of review process on a large scale could easily run into the tens of millions of dollars. In a sense, the funding agencies can’t really win. On the one hand, if they only ever pilot new approaches on a small scale, they never get enough empirical data to confidently back major changes in policy. On the other hand, if they pilot new approaches on a large scale and those approaches end up failing to improve on the current system (as is the fate of most innovative new ideas), the funding agencies get hammered by politicians and scientists alike for wasting taxpayer money in an already-harsh funding climate.

I don’t know what the solution is (or if there is one), but if nothing else, I do think it’s a good thing that NSF and NIH continue to actively tinker with their various processes. After all, if there’s anything most researchers can agree on, it’s that the current system is very far from perfect.