These outcome studies are gravely flawed for several interlocking reasons. (1) The physician (often famous) is exceptionally skilled. (2) The patients (who have sought out this famous physician) are exceptionally motivated to report a good result. (3) The indications for treatment are murky … which allows the physician to select patients that (s)he intuitively expects will do well. (4) The criteria for medical success are determined ex post facto.

The predictive value of such studies is nil. These flaws are summarized in the surgical aphorism “The best way to obtain reliably good results is to operate upon patients who don’t need surgery.”

This is not to say that graduate study has no value … my own experience is that graduate study often has great value. Roughly speaking, this value seems to derive 1/3 from the student, 1/3 from the teacher(s), and 1/3 from the community within which the students and teachers are embedded. This is true not just at the graduate level, but at all levels of education.

Our host in the OP said something like: “a PhD won’t hurt”. As someone else has already pointed out, that isn’t good enough. A PhD takes several years of a very smart person’s time, and the true cost of the PhD is forgoing whatever else you could have done in that time, which might represent several hundred thousand dollars in earnings. There had better be a payoff (professional or spiritual) that you couldn’t have gotten otherwise.

PS: love the active preview feature – neat!

It is very hard to give advice to people on what to do in the future. But it is somewhat easier to remark on what was done in the past. Very recently, Carlos Mochon (#37 in Preskill list or so) talked in our local quantum computation seminar about his wonderful solution giving a protocol for weak coin flipping with arbitrary small bias. It ain’t get much better than this!

I have been wrestling with a long postdoctoral / non-TT stint exacerbated by a two-body problem. I am on the market right now and have interviewed at mix of PhD granting research departments, and some teaching colleges and “terminal master’s only” universities.

I didn’t really enjoy my visits to the latter. You are lucky if you get one day a week for research at those places, and the classes that are taught tend to be lower-end intro and service classes. A professional master’s program increases the opportunities for more advanced classes, but the students are extremely vocational in mindset.

Anyway, if that’s what you are into, I think that the reason that I got the interviews with these places was having a lot of teaching experience with good teaching evaluations.

These places want instructors that can reach the students that are motivated but may not have the best background, and they want instructors that can teach across the departmental curriculum.

The more courses from the core curriculum that you’ve taught (as the instructor, not as a TA), and the better your teaching evaluations, the better your shot at appealing to a teaching oriented institution.

You are better off racking up your teaching experience while a grad student than running around taking a lot of VAP, lecturer, or adjunct positions.

(1) Your dream is achievable.

(2) It won’t happen spontaneously.

(3) For practical help, read (e.g.) A PhD is not enough (your library will have a copy.

(4) Also read E. O. Wilson’s autobiography Naturalist.

(5) Then talk to three people (or more) who have succeeded in the arena where you wish to succeed. Respectfully ask them how they did it. Then, do the same.

If you think this adds up to a mixture of common sense, careful planning, and hard work … yep, that’s the advice.

Well this is a really important question to address.

I love to teach as a Teaching Assistant, I am working on my phd in Physics at a reputed place. My question is:

What are the odds of getting a position in a non phd granting college/master’s degree insitute.
I love to teach, will be connected with my field and will have
adequate freedom to pursue what I want to?

Please address the above question as I think this would address huge fraction of people who love their phd subjects and want to keep pursuing it.

regards,
steve

That’s a great idea, Ralph! Here (from pages 487-8 of States of Matter) is a 1975 Goodstein passage that provides a starting point for answering your question.

When a theory is first proposed, the exciting thing is to show that it it right. … and so, unless the idea is a bad one, there is a tendency at first to confirm it. As evidence in its favor accumulates, the idea becomes better established, and at this point, if its correctness is not yet proved beyond doubt, its importance certainly is. The idea becomes a part of the received wisdom. Now an important inversion occurs. It becomes more exciting to show that it is wrong. Further verification would just be adding points to a well-known curve, but now falsification amounts to combating prejudice that stands in the way of new insights. All those factors that at first tended to ensure verification of the idea now operate in reverse, allowing one to doubt the original interpretation, allowing scope for an attack on the entrenched idea. At this point, with clever, resourceful, passionate advocates operating on both sides of the issue, one as reason to hope that some approximation to reality will emerge.

Now it’s thirty-three years later. What fields of science have undergone (or are undergoing) the “Goodstein Cycle”?

Example 1: Is biology experiment-driven, or is it theory-driven? This dilemma was much-discussed in the decades 1970-1990, and recent developments have resolved it by triangulation. Disciplines like biology, astronomy, geophysics, etc., are neither experimental nor theoretical, but rather, are observational. The Human Genome Project and the Sloan Sky Survey were the first examples of observational Big Science … but they will not be the last.

Example 2: Has science’s “Big Crunch” ended? Here by “Big Crunch” we mean Goodstein’s 1994 assertion that “The expansion of science … was guaranteed to come to an end.” Without belaboring the point, I will offer for reflection the idea that the Big Crunch has ended; that the exponential growth of science has resumed; and that this new epoch of exponential growth is being driven by a new kind of “Big Science” whose foundations are largely in observation and simulation, with a healthy admixture of theory and experiment.

Example 3: Are quantum systems hard or easy to simulate? This question comes closest to matching the Goodstein Cycle. For several decades the received wisdom has been that quantum systems are infeasible to simulate with classical resources … and this is surely true of systems that have zero noise and/or are error-corrected. But in the last few years, the appreciation has grown that this received wisdom is logically consistent with a world in which quantum systems that are noisy and not error-corrected are generically feasible to simulate. This latter case includes all systems that exist in nature, and all existing instruments that observe these systems, and all computing engines that simulate these systems … so it is a pretty important practical case (that IMHO turns out to have its own mathematical charms ).

Examples 1-3 dovetail nicely, and they collectively suggest a well-posed topic for debate.

Affirmed: The Big Crunch of science and technology is over, and a new epoch of exponential growth is underway.

This exponential growth is being realized mainly in the context of a new generation of “Big Science and Engineering” projects, that are founded largely upon observation and simulation, with a healthy admixture of traditional theory and experiment, and strong elements of engineering design too.

The field marks of this emerging paradigm of “Bigger Science” are massive observational databases, whose raw data is supplied by quantum-limited instruments, which are queried via well-validated simulation algorithms running on energy-efficient computers. These “Bigger Science” projects represent a new approach to coupling fundamental science to urgent societal needs (that’s where the money comes from).

Your mileage may differ, but the main point of this post is simply to note that if we assume that Bohr’s Principle “The opposite of a great truth is another great truth” applies to Goodstein’s Big Crunch essay, then we are led to a set of conclusions that is very good news for young mathematicians, scientists, and engineers.