You didn’t want to mate with that mantis anyway, trust me, bro. I’ve got your back.

I mean, true heat death would also imply that even your body is spent. No neurons will be able to fire. No brain activity. You won’t be any different than dead.

Skating’s rad. Longboarding is sweet. Rollerblading is tiiiiight, yo.

Just get the protectors and you’ll be fine. Elbows, knees, helmet, wrist guard, and (depending on your age, if you’re older than 12 you’ll want) ankle reinforcement. If you really want to go all out, hip, back, and tailbone pads are cheap and still not constricting. Are you going to look goofy? Sure. Is everybody else just as goofy? They’re wearing clothes, aren’t they? Of course they’re goofy. Just make sure the helmet covers the parts of the head that are going to get hit, not just the top.

It sort of looks like the little guy is wearing a turtleneck over his mouth.

…wait, turtleneck? Oh god, look again, now it’s wearing the shell of a koopa that’s been cut in half!

Replace the background with the Sierra logo and you’d complete the 80s-90s kid nostalgia.

finding out what doesn’t work is nearly as valuable as finding what does

Sometimes, sure. Most of the time, though, it’s more akin to: “Worked on isolated cells in vitro, but doesn’t approach target cells in vivo due to ECM.”

Remove the historic paintings during renovation, at least. Surely it would be possible to rig up some sort of sprinkler system as well. Firefighter access to the roof may be difficult once the fire is blazing, but maybe some mitigation systems could be installed before the blowtorches and welders come out.

I can’t wait for the inevitable r34 comments about how you feed this monstrosity.

Yes, but remember that you’re dealing with MBAs who make it their sole purpose to save pennies. Pennies saved on a few million cars equals more than their salary, which means they keep their job. So fuck a few people dying.

There was an off brand selling something called maple cremes. Cookies were in the shape of maple leaves, and the frosting in the center was just a touch off of brown sugar goo. They were good.

I think most science books are understandable by laypersons, except those that are memorization heavy, like biochemistry, or organic chemistry, or some parts of things like microbiology and pathophysiology. Statistics books and research design were pretty understandable, except for the actual math, heh. There really needs to be a push for people to read them casually, and encouraged to just stick to the concept parts and ignore the math and memorization of minor stuff. The free textbooks out there (I think openstax is pretty good, personally) are getting to the point where I think people might read them just for the ‘ooh’ part of science. Heck, it’s why psychology is such an enticing subject in the first place; it’s basically the degree of human interest facts.

I just thought that understanding the way the null hypothesis is used is important to really grasp what information the p is really conveying.

:D And for the parts about self reporting bias, and definitions and such, I was really, really having to hold myself back from talking about what makes your variables independent or dependent, operational definitions, ANOVA and MANOVA and t-tables and Cohen’s D value and the emphasis on not p but now the error bars and all the other lovely goodies. The stuff really brings me back, eh? ;)

To expand on the other fella’s explanation:

In psychology especially, and some other fields, the ‘null hypothesis’ is used. That means that the researcher ‘assumes’ that there is no effect or difference in what he is measuring. If you know that the average person smiles 20 times a day, and you want to check if someone (person A) making jokes around a person (person B) all day makes person B smile more than average, you assume that there will be no change. In other words, the expected outcome is that person B will still smile 20 times a day.

The experiment is performed and data collected. In this example, how many times person B smiled during the day. Do that for a lot of people, and you have your data set. Let’s say that they discovered the average amount of smiles per day was 25 during the experimental procedure. Using some fancy statistics (not really fancy, but it sure can seem like it) you calculate the probability that you would get an average of 25 smiles a day if the assumption that making jokes around a person would not change the 20-per-day average. The more people that you experimented on, and the larger the deviance from the assumed average, the lower the probability. If the probability is less than 5%, you say that p<0.05, and for a research experiment like the one described above, that’s probably good enough for your field to pat you on the back and tell you that the ‘null hypothesis’ of there being no effect from your independent variable (the making jokes thing) is wrong, and you can confidently say that making jokes will cause people to smile more, on average.

If you are being more rigorous, or testing multiple independent variables at once, as you might for examining different therapies or drugs, you starting making your X smaller in the p<X statement. Good studies will predetermine what X they will use, so as to avoid making the mistake of settling on what was ‘good enough’ as a number that fits your data.

While high fructose corn syrup isn’t great for you, it’s clearly not the problem. The US domestic use of HFCS peaked in the 90s, yet obesity has continued to skyrocket.

Ah, the ole ‘courtesy sniff.’