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How Scientists Know An Idea Is a Good One
Physicist Chris Lee explains one of the toughest judgment calls scientists have to make: figuring out if their crazy ideas are worth pursuing. He says:
"Research takes resources. I don't mean money—all right, I do mean money—but it also requires time and people and lab space and support. There is a human and physical infrastructure that I have to make use of. I may be part of a research organization, but I have no automatic right of access to any of this infrastructure. ... This also has implications for scale. A PhD student has the right to expect a project that generates a decent body of work within those four years. A project that is going to take eight years of construction work before it produces any scientific results cannot and should not be built by a PhD student. On the other hand, a project that dries up in two years is equally bad. ... the core idea also needs to be structured so, should certain experiments not work, they still build something that can lead to experiments which do work. Or, if the cool new instrument we want to build can't measure exactly what I intended, there are other things it can measure. One of those other things must be fairly certain of success. To put it bluntly: all paths must lead to results of some form."
That's not a description of a good idea. That's a description of an idea that fits into an arbitrary 4-year timescale that fits with a PhD program's average length.
Just because you're paranoid doesn't mean there isn't an invisible demon about to eat your face
The triangle of supply and demand works in this case as well.
Good/ Fast/ Cheap
Pick any two. Then use the profit algorithmic function to determine if the time utilized is an asset or boat anchor.
*Repent!Quit Your Job!Slack Off!The World Ends Tomorrow and You May Die!
Science as a process is like Natural selection and just as in Natural selection, one may come with the dead end. This is not necessarily bad.
To quote Thomas A. Edison, "If I find 10,000 ways something won't work, I haven't failed. I am not discouraged, because every wrong attempt discarded is another step forward".
A PhD student has the right to expect a project that generates a decent body of work within those four years.
Four years? Ha! That's a good one!
A big part of the problem is that there are few negative results in scientific literature. Ever found a paper with a clear negative outcome? I didn't. This "positive bias" in scientific publications is probably causing a major blow to the efficiency of scientific research.
If Pandora's box is destined to be opened, *I* want to be the one to open it.
The good ones need ink as well.
It is obvious that you're a mathematician. Your equation is dimensionally wrong.
...and the ability to think on your feet.
It is not possible to plan 4 years ahead to ensure success. What you get instead is a PhD project plan that's wrapped in a set of general concepts (AKA escape routes) in case you hit a dead end. I'm currently doing a life science PhD and have changed tack at the half way point. A number of my colleagues have also, often quite drastically, whether for reasons of practical feasibility or time constraints.
If we know accurately what we were going to work on that far in advance, it has probably already been done.
Python coder | PyQt Applications | Writer
Ancient Persians would debate ideas twice - once sober and once drunk. It had to sound feasible in both states to be a good idea.
I'm afraid the title of your note is misleading. Good science, much more than good engineering, involves testing new or old theories, to find how they work in previously untested ways, or to make sure that the previous test was really valid and caught all the important factors. A good graduate school project, involves a constrained project that can be reasonably tested in a few years, that does involve something of interest to the adviser, and that with good luck can be turned into a career of related questions.
The key is to make the initial question relatively simple, so that the concept can be expanded into tests or other related fields as time and funding permits. This isn't asking the "right size" of question, it's asking a question with enough related, interesting implications but that still has relevance if only the simplest parts can be addressed. Let me take an example of something I'd love to find a good thesis for: the cost of using different sorting algorithms.
The maximum computational costs of complex sorting algorithms is well understood (and well described at Wikipedia). But the additional computational cost of maintaining registers is not factored in, especially for small or modest data sets, and the cost of comparison _itself_ between different formats, or between positive and negative numbers, is not factored in to those computational costs. Neither is the cost of a partial sort that has to be started over from scratch or the benefit of algorithms that can be used when it is partially sorted. There is _wonderful_ material for a thesis in that kind of question, and even material for almost immediate application to industry. The preliminary survey and testing work with computational models can be done within a year by someone competent, but testing it against different CPU or software environments would be even more valuable and could easily fill out the rest of a graduate program, even leading to a creer in optimization of computational algorithms.
It is obvious that you're a mathematician. Your equation is dimensionally wrong.
No, it's correct. Let's do the analysis: $= (time + obtanium) / desire * beer
time is in seconds
obtanium is in seconds (how long to obtain it)
desire is in seconds/liter (the longer you wait, the more you want)
beer is in dollars/liter
so we have (seconds + seconds)/(seconds/liter) * (dollars/liter) = dollars
Q.E.D.
I feel sorry for people that don't drink, because when they get up in the morning, that's as good as they're gonna feel
Good ideas are hard to determine, and sometimes you find out something was actually a really bad idea only after several years like trans fats, or saccharin.
The results of scientific discovery are diminished by classifying them as success/failure. The only 2 classifications should be "A Truth Discovered" or "Pseudo Science".
Any lab experiment which is conducted to seek the truth even if it does not yield a commercially viable result is still a truth discovered. A so-called failed experiment still is a success at discovering a method which does not work to achieve desired results, and discovering what does not work in some cases can be more important then finding out what does and is an actual truth discovered.
Any experiment performed to skew results in a particular direction, or where evidence is tossed that does not agree with your idea's is nothing but pure Pseudo Science. Unfortunately we have so much of this it has made people distrust scientists because they have proven that they are just as opportunistic as normal people and will do just about any dishonest thing for a buck! True Science be damned!
But that is theory. In practice, having some realistic goals based on available resources of money and time is common to all fields, not just science.
[*] Chandrashekar was not bitter about Eddington, he credits being forced to change fields in his late 20s, taught him how to learn and he deliberately abandoned his field of study about every ten years, he continued to be productive into his late 70s. If you find the spoof paper written in his style The Imperturbability of Elevator Operators, by S Candlestickmaker, by one of his grad students, it makes hilarious reading for the geeks. ]
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
To continue the CKXD comic,
Math is applied Logic
Logic is applied Philosophy
Philosophy is applied Sociology
and "the circle is now complete."
"Good/ Fast/ Cheap
Pick any two. Then use the profit algorithmic function to determine if the time utilized is an asset or boat anchor.
--"
Fast and cheap may be easy to measure, good on the other hand is not so easy.
For example, during the early years of the cold war it was thought that nukes would be a fast and cheap way to deal with a Russian invasion of Europe.
(and it would kill plrnty of commies, so it was obviously good as well, however the radiation and nuclear winter effects would have killed most of the rest of us, but they didn't know that at the time.
Four years? Not in Canada - and presumably not in the US either. The department average in my program was more like 6 (I took about 6.5), and I've known people who have taken as long as 10 to complete their PhD.
From some document I found on startpage: http://careerchem.com/CAREER-INFO-ACADEMIC/Frank-Elgar.pdf
"Median time-to-completion of the PhD has nearly doubled during the last three 2 decades (from 6.5 to 11 years). "
This is why it's so important in biology to know people, or to have a PI who does. Friends tell friends their negative results, and that's how word gets around.
The PhD students working on something like ATLAS certainly were working on projects that took less than 8 years to construct. Yes, they contributed to a larger project that took much longer, but what they were individually working on had to still fit in the timescale of a PhD program. Either you are purposely misconstruing what was meant by the story,, or simply not thinking when you typed out basically agreeing that a thesis project needs to have narrower scope to match the time requirements
The only way to know if an idea was good, is after you've already done it. Future prediction is always a crapshoot. People who claim to be good at it were typically just lucky, and are deluding themselves into thinking it was all skill.
"A PhD student has the right to expect a project that generates a decent body of work within those four years."
For a Masters degree, this is acceptable. For a PhD, they had better be coming up with their own idea, a plan, funding, and then have their advisor and committee evaluate during the prospectus defense. Having their topic/project dropped in their lap so they can turn the crank is not what a PhD is all about.
Funding?
There are areas of physics where the cycle time for proposals is 2 years (from announcement to release of funds) with a success rate of less than 10% for even senior people (NIH has an even lower funding rate, and an expectation that most things get proposed a couple times before being funded). Many, if not most, graduate students in science can easily get funding to cover their salary through fellowships/RA positions/TA positions, etc, but the chances of a grad student writing their own grant proposal in most subfields is pretty small. Sure, there are areas where you can do good science with dimestore materials (and a few places that specialize in that), but that's a pretty narrow slice of science in almost any field. Some of the most successful faculty I've known at one of the top science/engineering universities in the world are successful because they let their post-docs be PI on proposals (which is relatively uncommon). Then if the project is awarded the post-doc starts the work as a post-doc and manages to spin it into a faculty job.
That's why everything is fast and cheap.
Sleep your way to a whiter smile...date a dentist!
Scientists tell if an idea is a good one by trying to prove it wrong, over-and-over-again and in as logical a thought-out way as possible, til they give up. This is known as "science", and the fact that they do it this way is why we call them "scientists".
If you think that "scientists" are mostly after money, then you don't know anything about how science works or where funding for science is actually spent.