Comment by whatisthiseven | Hacker News Reader

Comment by whatisthiseven | Hacker News Reader

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I keep seeing this sentiment when it comes to those in the natural sciences, but it makes no sense.

I could replace "programming" in your above little bit with "mathematics" and it would be just as weird.

Our modern world runs on computers and programs, just as our modern world and modern science built itself on mathematics and required many to use it. So too the new world of science may require everyone to know to program just as they know about the chemical composition of smells, or the particulars of differential equations, etc.

And I know your argument isn't "they shouldn't learn programming", but honestly since I keep seeing this same line of reasoning, I can't help but feel that is ultimately the real reasoning being espoused.

Science is getting harder, and its requirements to competently "find the exciting things" raises the bar each time. I don't see this as a bad thing. To the contrary, it means we are getting to more and more interesting and in-depth discoveries that require more than one discipline and specialty, which ultimately means more cross-functional science that has larger and deeper impacts.

Most scientists are not great at math either.

Again: these are tools that are means to an end. They only need to work well enough to get the researcher to that end.

A lot of what are considered essential practices by expert programmers are conventions centered around long-term productivity in programming. You can get a right answer out of a computer without following those conventions. Lots of people did back in the day before these conventions were created.

That's not to say that everybody with horrible code is getting the right answers out of it. I'm sure many people are screwing up! My point is just that ugly code does not automatically produce wrong answers just because it is ugly.

By analogy, I'm sure any carpenter would be horrified at how I built my kayak rack. But it's been holding up kayaks for 10 years and really, that's all it needs to do.

I will add that in general, statistical analysis of data is not by itself adequate for scientific theory--no matter how sophisticated the software is. You need explanatory causal mechanisms as well, which are discovered by humans through experimentation and analysis.

And you can do science very well with just the latter. Every grand scientific theory we have available to us today was created without good programming ability, or really the use of computers at all. Many were created using minimal math, for example evolution by natural selection, or plate tectonics. Even in physics, Einstein came up with relativity first, and only then went and learned the math to describe it.

Your point is maybe a little obtuse to me, because it sounds like you are arguing for "computers are tools that should be learned, but really no one does and who can blame them, they just want to science" and simultaneously arguing, "tools aren't science, and science can be done without them".

I feel like the later is obvious: of course the tools aren't science, but if you want to do real work and real science, your tools are going to be crucial for establishing measurements, repeatability, and sharing how one models their hypothesis onto real world mechanics.

Likewise, the former is just the same commonly repeated thing I just argued against and my reply is the same: so what? You building a kayak is not science and is irrelevant.

Scientists can't reach a meaningful conclusion without proper use of tools. All they can do is hypthesize, which is certainly a portion of science (and many fields are in fact stuck in this exact stage, unable to get further and come to grounded conclusions), but it is not the end-all of science, and getting to the end in the modern day science means knowing to program.

Of course there are exceptions and limitations and "good enough". No one is arguing that. The argument I am refuting is those who think "tools are just tools, who cares, I just want my science". That is the poor attitude that makes no sense to me.

> Scientists can't reach a meaningful conclusion without proper use of tools.

I'm just trying to make the point that "proper" is subjective. Software developers evaluate the quality of code according to how well it adheres to well-established coding practices, but those practices were established to address long-term issues like maintainability and security, not whether the software produces the right answer.

You can get the right answer out of software even if the code is ugly and hacky, and for a lot of scientific research, the answer is all that matters.

The usual reason programmers object to ugly, hacky code is that it's a lot harder to be justifiably confident that such code actually does produce the right answer -- "garbage in, garbage out" is just as true in function position as it is in argument position.

Tbh I think its a case for multidisciplinary research. You wouldn’t only hire one skill set to run a company, even a tech one, so why should research be any different? That’s probably where the deep insights are.

People that are just decent programmers can make at least twice (probable 3 or 4 times) as much money working for industry than for science in an academic environment. Most programmers that would work for less money because they are interested in science will be more interested in computer programming problems than basic programming to support a scientist. NSF won't give you $250k to hire a really good programmer to support your gene analysis project. More like 100k if you are lucky.

So what you end up with are that great scientists that are decent programmers are the ones who can do the cutting edge science at the moment.

That's a problem we should aim to solve.

Think of the flip side: Programmers are terrible biologists.

Sure, it would be great if we all had more time to learn how to code. Coding is important. But I'd say the onus should be on coders to build better tools and documentation so they are empowering people to do something other than code, rather than reduce everything to a coding exercise because making everything look like code means less boring documentation and UX work for coders.

I mean, biology is in fact a full on degree program and you pretty much need a PhD before you're defining an original research topic. It's not because biologists are dumber and learn slower. It's that biology is complicated and poorly understood, and it takes years to learn.

Contrast this to coding... you don't even need to go to college to launch a successful software product, and the average person can became proficient after a few years of dedicated study. However, this is a few years that biologists don't have, as their PhDs are already some of the longest time-wise to finish.

The decision to rename genomes is totally consistent with the biologists MO: if a cell won't grow in a given set of conditions, change the conditions. Sure we can CRISPR edit the genes to modify a cell to to grow in a set of conditions, but if it's usually far easier to just change the temperature or growth media than to edit a cell's DNA.

My take away is that this is more a failure of programmers and/or a failure of their managers to guide the programmers to make tools for biologists, than of biologists to learn programming. Sure, coders get paid more, but they aren't going to cure cancer or make a vaccine for covid-19 without a biologist somewhere in the equation. And I'm glad the biologists developing vaccines today are doing biology, and not held up in their degree programs learning how to code!

MatLab has taken over bio specifically because it has great documentation and examples. If Python was psuedo-code that compiles, then MatLab is just speaking English. Even still, the spaghetti that researchers get into is just insane.

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