How to think about science and becoming a scientist
jseliger.com
jseliger.com
Scientists and engineers expand the size of the economic pie; lawyers mostly work to divide it up differently. Whenever possible, work to be a person who creates things, instead of a person who tries to take stuff created by someone else. There is an infinite amount of work in science because the universe is big and we don’t really understand it and we probably never will. New answers to questions in science yields more questions.
Greenspun makes and attend to his discussion of what grad school in the sciences is like, especially this, his main point: “Adjusted for IQ, quantitative skills, and working hours, jobs in science are the lowest paid in the United States.”
In other words, science is good for society but bad for the individual, from a purely economic standpoint.
I'm completely open to an alternative service (or even a complementary one). So... I guess what I'm trying to say: Give me, one of your intended customers, a compelling use scenario or value-add proposition that might compel me to switch.
FYI, adjusting for certain variables is a fundamental aspect of statistics. All studies in social sciences and economics of real-world data do so, because no real-world effect can be isolated to the point that it can be measured independently.
"Haven't we reached the point yet where the whole idea of IQ testing is pretty discredited?"
What? No, of course not. Are you saying that there are no people who are smarter than others?
For IQ to simply mean the intelligence scale normalized so that average intelligence = 100 would take a big marketing effort amongst the education and psychology communities.
Even if it were divorced from the twists and turns of its historical development, it seems pretty clear that at it's best IQ can aggregate the values of creativity, lateral thinking, calculation, memory-retrieval, memory-storage, memory-organization, (even, despite tester's best efforts) domain knowledge together and replace them with one number.
I think that some of the "everybody learns differently!" stuff has jumped the fence and become an old wives' tale, but there has to be a happy medium between assigning someone a 40 column printout to summarize their intelligence and slapping one number on it.
[This is not to mention all of the shift in emphasis away from intelligence towards results and output based partially on Outliers, and partially on the idea that if you praise kids for an inherent trait that they have no control over that they will stop playing to win and start playing not-to-lose.]
It's not like we're talking about one specific methodology for measuring IQ. The whole argument is in the context of the OP arguing that correcting for intelligence is necessary for making a meaningful comparison between wages earned (basically, it's discounting for opportunity cost). Which is totally reasonable and obvious.
My argument is that above average and even extraordinary intelligence have no correlation to money making. Smart people live in poverty all the time. To say that expectations of earned income should be adjusted for IQ is meaningless in that context because the adjustment would be zero. Making money doesn't derive from general intelligence, but from how it is applied and luck. There are many smart technical people on this message board who are clearly lucky that the world is in the middle of a massive expansion of technically complicated economic areas like apps and programming in general. It allows them to achieve wealth that otherwise is not a predetermined given.
When I refer to IQ testing I refer to the concept of a universalized IQ test that can, without cultural bias, give an objective measure of intelligence.
Well you're objectively wrong. Go to your national data office and look at income vs education (using education as a proxy for intelligence is not perfect but works OK enough for this purpose). You will see strong correlations between the two. (not perfectly linear, and not perfectly correlated, but strong enough to be not random). Look at any data set of reasonably stable and free countries that have these two data points, and you will find the same.
"Smart people live in poverty all the time."
Anecdote != data.
No, the opposite! Throw together any list of questions requiring intellectual ability, on any subjects you choose (making sure to have a wide range of difficulty). Use the list to test a few thousand people selected at random from the same society. Perform principal components analysis (a type of statistical cross correlation) on the answers. The answers will turn out to correlate with a single characteristic of the individual test takers. This common factor is labeled g, general intelligence. There is virtually no sign of multiple intelligences or other factors, just one honking big signal for the g factor.
> And adjusting for IQ, meaning that someone is smart therefore they are expected to earn more money?
There have been large studies of people from the general population, with the scientists measuring every data point they can lay their hands on. It turns out the only factor that significantly affects adult income is IQ. Earned income is almost totally uncorrelated with race, skin color, culture, family wealth, family social rank, location of residence, school system, characteristics of siblings, and so forth. IQ also strongly predicts criminality.
Studies of twins separated at birth show huge IQ correlations between identical twins, but IQ correlations between fraternal twins are no greater than for sibling pairs from different pregnancies. So IQ is mostly inherited, and mostly fixed by the time of conception. In other words, genetic.
My old company made what was essentially datalog viewing software. Version 1 of the software was basically just squiggly lines going across the screen. People loved it, because previously they only had squiggly lines going across paper, and a 20-minute recording was the size of a phone book. But version 2 of the software required far more developer work, and had much more polish and detailed algorithms underlying the analyses... but people bridled at 'having to pay again' when 'we don't really get that much for it'. More work, smaller bricks, less appreciation of what it takes to get there...
John LaMattina, former president of R&D at Pfizer, weighs in: http://www.forbes.com/sites/johnlamattina/2012/03/02/there-h...
There may be an infinite amount of work in science, but there is a finite (and very unevenly distributed) number of grants. Your conclusion rings true to me.
therefore:
Degree Classes != Actuality.
Caveat: I haven't been a practicing scientist for a while.
In other countries, there are explicit and implicit reminders that science students are smarter than non-science students. In my dad's (European) high school, students were literally tracked from A to F; A was math, B was physics, biology and social sciences were somewhere in the middle, and vocational school was at the bottom. Harsh? Yeah. But it meant that generically smart and ambitious kids wound up in the hard sciences by default.
That being said, I agree with the sentiment. Most of what we teach undergraduates is about the knowledge science is produced, rather than about the process of doing science itself.
Sure, the article is about about pursuing science rather than thinking about it. But that's the author's whole point. Enjoying a career is all about enjoying the day-to-day work: If you love thinking about DNA but don't love pipets, you're going to be unhappy a lot of the time, because life in the lab is about 10% deep thought and 90% pipets. (Or, in the semiconductor laser lab: 10% deep thought, 50% misaligned optics, and 40% mysterious process problems that you will never entirely understand, but which you will eventually solve by spending months on end turning knobs in a strategic manner.)
Same general advice applies in engineering: do your best to attach yourself to a lab, and see if it catches your fancy. Best way to test-drive a career choice.
He's kind of down on textbook-and-problem-set coursework and large lecture classes. This is not universal. Some large lectures are large for a reason -- the professor is a star. And some textbooks are really good, and some problem sets are worth sweating over.
Which is not entirely a useless endeavor; if you don't know about what has already been discovered, how can you build on it and go further? How will you know what has already been tried?
It is also the last time you get to play with lab equipment that costs hundreds of thousands of dollars.
Absolutely no regrets.
Instruments start at 250k and many in universities are decades older than what is currently used in industry. For small molecules, the most challenging (and exciting) library's of compounds are owned by third parties.
It is my opinion that science, much like programming, one has to do it to learn it. There are limitations to the knowledge you gain without being hands on. See E.O Wilsons musings of the impact his formative years and post grad school wanderings in the south pacific. He is certainly a scientist but much of his knowledge and insight is due to the hands on approach he used to to gain that knowledge.
Then there is the electrical bill and I already get hell from my wife for the water bill during the summer months.
The ones I know who are actually grad students or professors tend to take a much more pragmatic view.
I wrote this in another thread: "'Science is a wonderful thing if one does not have to earn one's living at it.' -- Albert Einstein" -- from Philip Greenspun's Women In Science: http://philip.greenspun.com/careers/women-in-science .
To be a happy professional scientist, the emotional stimulus of that kind of discovery needs to be strong enough to carry you through all the hardship.