[snowwrestler]

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.

[whatisthiseven]

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.

[snowwrestler]

> 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.

[kd0amg]

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.