Merci!
nickwitha_k
Merci!
It's been years since I've been in the lab but it really will depend a lot on the subject matter and the type of experiment.
If it's a subject matter that is fairly well explored and defined, the alternative hypotheses might be fairly straightforward. Take, for example, an experiment from a while ago where entomologists suspected that desert ants navigate by using dead reckoning, effectively counting their steps, remembering their changes in direction measured by a biological compass, and integrating them together, in a process similar to "fusion" in electronic position sensors.
To validate part of this hypothesis, they needed to get more granular and isolate one part of it. So, they formulated a "sub-hypothesis" that stated that the ants had some sort of innate awareness of the distance that they covered with each step, knowing the length of their legs and this their stride length, similar to how cats know their healthy body width. The experimental hypothesis would be something like:
"Altering the length of desert ant legs will result in navigation failure with longer legs causing them to overshoot and shorter legs causing them to undershoot. The navigational trajectories should otherwise be identical."
Building alternative hypotheses for this relatively simple experiment, prior to conducting it would be straightforward, as you appear to be suspecting. They could be as simple as:
"The length of the desert ant's legs will have no impact on their navigation because they are not directly related. This will be apparent through the ants showing no discernable difference in the paths that they take when navigating, regardless of leg length."
"The length of the desert ant's legs will have some impact on their navigation but, they are able to compensate for discrepancies in stride length through some as of yet unknown mechanism. This will likely be apparent in statistically significant distance-related navigation errors in their paths."
After the experiment, the data would be analyzed and checked for a match against the established hypotheses. If there is not a good match or there is an unexpected shape to the data, further experiments may be required to see if it is an anomaly or if something else might be going on.
(In this case, it was found that, yes, desert ants have some sort of innate awareness of what their stride length should be and changes in their leg lengths throw off their navigation, as expected.)
Now, when it gets to subjects that are less clear and established, alternative hypotheses can get a lot more challenging because often the difference between the data fit that proves or disproves a hypothesis can be miniscule. Or, the data points might form a completely unexpected shape that doesn't match currently known phenomena.
I honestly don't understand why everything has to be taken so goddamn uncharitably by the regulars on that instance. It blows my mind how they manage to always act in bad faith. Always.
It's pretty straightforward, imo, they built a feedback loop into their instance culture that encouraged social dopamine junkies to participate in toxic behaviors, valuing things like "dunking", othering, and dehumanizing the out group (non-hexies) over things like factuality, good faith, and not being dicks.
The UK government has been systematically destroying its healthcare system for a while now.
Hey. How'd you get my notes?...I forgot where I put them so if you have some kind of trick or magic, it would be most helpful.
Fuck off and give me the fiber that was promised and paid for decades ago.
I may not know that many Jewish people but all of my hebros and hebroettes oppose genocide.
In my experience often detriment. Most of the images for projects that I have been encountering as of late - hell, most Dockerfiles that I've been encountering - have hardware-specific config and packages. I just want a Dockerfile or maybe a docker-compose.yaml that is hardware neutral by default and doesn't use the shitty throttled Dockerhub for its base image.
#!/bin/bash
# Build image and push to registry
docker build -t myproj:latest . && docker push myproj:latest
you answered my second question but not in the way I intended, I meant to ask for more of a methodology like, do you just read the man pages? do you refer to AI? are you just full trial and error? does your work provide resources? Im asking because I generally want to see why its such an issue for people to find info, personally I use a mix of selfhosted AI and various forums and wikis. I wouldn't be supprised if some users are learning 100% through chatgpt or a single youtube channel.
My recommendation would vary depending on use case.
If just gaming, yeah. Your approach sounds sane.
If wanting to tinker, develop, or, honestly, even do stuff like deploying local LLMs and the like, I would strongly encourage gaining familiarity with manpages. For anytime where precision and accuracy are necessary, like low level tinkering, I don't believe that should trust LLMs. Learning how to find relevant info in manpages and dev reference materials will save a huge amount of time and heartache.
Thank you!