You can add to this that if you are in a facility with both rats and mice, you should never house them in the same room, and you should probably not enter a mouse room after being in a rat room. In the wild, rats are predators of mice.
But in the wild, there aren't separate rooms for predators and prey, either. If laboratory rodent experiments are so reliant on such synthetic conditions, what's the point?
The point is to isolate variables. If you want to see whether X might be causing Y, you can't just do X in random environment while A, B, C, D and Q is happening, and claim if you observe Y that was because of X. You have to isolate X and establish a link between environment with X producing Y and the same (to the extent possible) environment without X not producing Y. Then you can make a step (still not enough to be sure but at least to start suspecting) that X is causing Y. Of course, it could turn out that X is causing Y only when A is present but B is absent, and then your experiment will be a failure. Or maybe Y just randomly happens and you had back luck to land on it exactly when you did X. Nobody said it's always easy :)
If some mice are housed near rats and others are not (or simply vary in how far they are from the nearest rat), that will introduce variation into your measurements. For experiments not specifically about mouse/rat interactions, this variation is irrelevant noise that makes it harder to detect or characterize the effects the experimenters actually care about.
That's not my point, though. Like, if you conduct physics experiments in a vacuum because interactions between objects and atmosphere isn't part of what you're studying, that's fine. If everyone in physics is conducting experiments that way, then suddenly you're left with the question of, it turns out that in real life I encounter atmosphere all the time, and how much can these experimental results tell me about the world I actually interact with? And if it turns out that all the preconditions to physics experiments can't be published because there are too many of them to list, and you're just expected to know these things, doesn't that throw the credibility of the whole enterprise into doubt? Controlling for variables is fine. But if you aren't comprehensively listing all the variables you're controlling for, if the very idea of doing so is considered a fool's errand, then are you even really doing science?
> "if you aren't comprehensively listing all the variables you're controlling for [...] then are you even really doing science?"
Mate I think that's the point of this whole thread that you're commenting in. And the tangential point to the article posted.
Science isn't some binary thing. You can do poor science, and you can do great science. Some variables are hard or impossible to control for. Some fields make this simpler than others. I'd say that as we've continually endeavored with the sciences we're probably better at it now than we've ever been before.
Synthetic conditions are absolutely critical to science. Typically, the more conditions you can specify in the experiment, the more reproducible it should be. Some of these are very difficult, and others in the thread have pointed out that some don't get labeled in the journals.
If we ran such experiments in the wild, completely outside of control, then we can never know what we're really observing. By controlling the environmental variables your observations gain meaning.
Yes, because you are trying to control your variates? Just because physics is the most amenable to experimental control does not mean that the only real science is physics.
I mean by that measure, medicine is not a science either because we don't know most of the possible confounding variables. That doesn't mean that attempting to use the scientific method still isn't the correct choice.
> Just because physics is the most amenable to experimental control does not mean that the only real science is physics.
That's not what I'm trying to say here at all. The point isn't about how amenable you are to experimental control. The point is that even when experimental control is easy to isolate out, like in the physics example above (which I don't think is true in all of physics, by the way), it's not free. You're trying to compensate for the lack of available statistical power to measure an effect in noisy data by cutting down on the noise in the data. But you're doing it by generating the data in an environment that doesn't exist outside of laboratory conditions. Writing off replication failure as not being a problem because lab conditions are difficult to reproduce misses this; if the findings are difficult to replicate in other conditions, that could indicate that the findings are more narrow in scope than the study suggests. As I pointed out downthread, for example, if all the rodents in an experiment on a drug are on the same diet, all the experiment proves (assuming it's otherwise well run) is that the drug works in combination with this diet. If the drug works independently of diet, then the findings on the drug are generalizable. If it doesn't, though, they aren't. And if you have 60 years of medical research based in part on studies with rodents who eat diets very differently than what rodents eat in the wild, or what people eat, then it raises all sorts of questions about the state of medical research. That doesn't mean that medicine isn't a real science, it just raises questions about how well it tells us what we think it's telling us.
You've gotten a couple of answers, but my answer might rely heavily on my field. I work in early stage pharma discovery research. So our goal isn't to determine the basal level of some cytokine, for example, in normal mice. Our goal is typically to see that cytokine's response to treatment, or stimulus, or stimulus then treatment. In other words, the mouse is a living system one step more complicated than a dish of cells, which is its value to us. Sometimes, once you take a drug to non-human primates or humans, you have to drastically change the type of study you run to determine efficacy.
