Jesus, my friend cycles between mechanical efficiency and sexual orientations like a clown in a stripper juggling show. Now I know why!

I think that’s what bothers me. From the picture, you can tell the lady is wearing an outfit. The gloves, the hat, the boots, the scarf; they are all pulled together. From the back, it looks like the guy’s coat is not as striking and coordinated. Maybe it’s just the orange/cream shoes, blue socks, and khaki pants rolled up, maybe it’s because the picture is from the back, but it looks like he’s wearing a bathrobe while she’s wearing a coat.

A shooter might be hard, but I think you could make ORION: Prelude fit. It’s a little old now, but still fun. You get to shoot dinosaurs and run around big maps.

Other games that I’d recommend: Avorion (build your own spaceships and fight and befriend the galaxy) It’s not technically a shooter, but depending on whether you pick out lasers, electric tasers, machine guns, or rail guns, there is definitely some aspects of moving to avoid enemy fire while plunking them down. [but don’t be like me and just make lots of borg cubes… because cubes are the best!]

Ark: survival evolved (tame dinosaurs and fight the map)

Elden Ring with seamless Coop mod (up to 6 players at once)

War Thunder for plane sim, World of Warships for good ship battles

Valheim is also capable of being extremely frustrating, so I’d recommend one of the mods that removes the skill penalty loss when dying. It might not be necessary, but dad knows the kids’ temperaments better than me.

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.

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.