hey, larian. just curious. where did you get the data for the most commonly chosen faces you use as an excuse to not have more customization? how many of those people were queer? were trolls?? what was the age range? you probably don't know, since obviously you couldn't know unless you spied on every person in your hopefully large sample size. when getting statistics for something, it's good to consider what people you're not including and somehow try to fix that. or at least ask team members of minority groups what they think?? also also, let us use all heads on all body types you cowards. urgh i've been trying to find a mod for this oversight pretty much since the game released and all i've found are replacers, which aren't ideal.
“Hate crimes aren’t increasing, those are all hoaxes”
People are actually saying this shit.
despite the fact that they have actually 0 amount of intelligence at all.
They provided a list (not reading every instance, so I’ll take them all to be accurate as “false report/hoax”) of hoax hate crimes. But they failed to literally read the second sentence of the source I provided.
Look at the second sentence.
7,100 REPORTED HATE CRIMES IN 2017.
Which means in 2016 there were at least 6,068 reported hate crimes.
Their source has a total of 349 hate from 2012-2019.
There were 53 hate crime hoaxes in 2017.
For the record, that means .746% of hate crimes reported in 2017 were hoaxes.
They’re shouting right-wing bullshit talking points out their ass and they believe it wholeheartedly. People legitimately think 53 is a significant amount of 7,100+. That 53 cases a major factor when it’s less than 5% of the increase, when there were 40-something hoaxes in 2016.
So about a dozen more hoaxes in a year is, in their heads, somehow relevant to and representative of an increase of about 1,100.
Researchers from UC San Diego found that teenagers who begin using cannabis show slower gains in thinking and memory skills as they grow.
Adolescents experience extensive neurocognitive development, with cannabis use potentially impacting developmental trajectories. Here, we comprehensively assess the influence of adolescent cannabis use onset on neurocognitive trajectories and consider how recent delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) may influence neurocognition. We use the large, diverse longitudinal Adolescent Brain Cognitive Development (ABCD) Study dataset, combining self-reported substance use with objective toxicological tests (hair, urine, breath, oral fluid). Longitudinal mixed methods of the full cohort (n = 11,036, ages 9-17; 47% Female/53% Male) investigate time-varying cannabis onset on neurocognitive performance. Primary model covariates include sociodemographics, family history of substance use disorder, prenatal substance exposure, early psychopathology, other substance use, and nesting for participant ID, study site, and family ID. Secondarily, in participants with repeat toxicological hair testing (n = 645; 38% Female/62% Male) at ages 12-16, we consider the influence of THC v. CBD v. Controls. Primary models included false discovery rate corrections (FDR-p < .05) while secondary models were interpreted at p < .01. Cannabis group interacted with age to show altered neurocognitive trajectories across domains (immediate recall and delayed memory, processing speed, inhibitory control, visuospatial processing, language, and working memory; βs = -0.11- -0.52). Secondary models indicated hair-identified THC exposure*age predicted worse episodic memory than in Controls (β = -0.60, p = .007), with no difference between CBD exposed and Controls. Data suggest those who use cannabis show likely pre-existing better cognitive performance during late childhood, with reduced improvement or flattened trajectories over time. These neurocognitive trajectories in youth (ages 9-17) who initiate cannabis use were demonstrated after accounting for within-person change and numerous known confounds and improving accuracy in identifying cannabis use through incorporating toxicological measures. Continued monitoring of this cohort will clarify cannabinoid-cognition relationships into young adulthood, including the impact of timing of cannabis use initiation.
covariates is not saying it includes this in the statistical modeling & total affect & effect of the study over time for, including other co-morbidity factors. For example, did you test for poly-substance use such as alcohol? Do you have any objective measurements to the end of concluding it was & or wasn't used? Often, they go hand in hand with secret substance use away from authority & or parents. How much of cannabis was used & what is the route of administration? Both affect & effect likely outcome. A higher amount of co2 & co (carbon dioxide & carbon monoxide) will automatically do the same as these "modest differences" in the memory & more outcomes.
This study, when did it happen? During multiple red state runs in the oval office? That is another model that isn't accounted for in your statistical analysis here. Sociodemographics here show off a worsening across the board during their terms, with knowledge of how petty teachers are & even parents, those children can be exposed to points where they simply suffered from issues with their teaching & or parenting in that time, same for a parent's use suddenly coming out later & or relapsing which can cause the same at home turbulence linked to the same issues mentioned in this study.
Covariates doesn't mean it was adjusted, doesn't mean it was followed at times of these issues within the total scope, doesn't show delta changes to when they are brought in to have those change & or alter "predicted results" which are based on others that don't have the same DNA, brain make-up, good health & care standards, & even sleeping & eating habits in other children.
If they felt this is fun, had access to it, a lowered economic status may be there, to a higher one where a prevalence of being seen above others & not needing to try exists. Both already known to decrease metric over time for these issues presented.
Neurocognitive trajectories turns into a problem based on perceived averages that don't actually matter towards the ones being measured as they are based on math & numbers that aren't relevant to the ones being seen & measured.
Is comparison in a way done poorly. Non-relevant directions of others used to convey "should have gotten to" milestones of those measured in this study, literal irrelevance.
Just like stuffmadehere shows off, statistical models in a given physics sim can't be used to get to those total values of difference, these absolute subjective & not correctly placed in values of total change & difference give you these results. This is bad math & bad science for entirely different reasons.
To anyone reading this comment, this is me slamming this study saying that other kids that did other substances & combined with others in better & or worse socioeconomic backgrounds have to be relevant statistically to this modeling to make it something that can even have that total difference be there. Which it isn't relevant. They aren't close enough to mean anything & its not even factored in, together with other absolutely important factors that need to be factored in properly & correctly (like above) to be used in the manner this is claiming it can say these results.
Together with above factors of multi use, total amount used, multi-substance, & other factors total value of alterations to the given outcomes believed to possibly happen based on relevant individuals that are close enough to then be used to adjust trajectory & averages to actually be useful in any meaningful way to then have it actually have those statistical outcomes be actual statical outcomes to care about not being done, this basically screws over their entire multiple years & over 11k children tested & used.
They made their study literally worthless & not accurate. It can't even be considered something that says maybe its bad & causes some issues like this. But the title says it can be & the abstract above tried to say it can, but it can't.
In highschool, a friend showed me a website, that would tell you how rare your name was based off the USA senses data
But it want a good model because it assumed that first and last name where independent variables.
So it took the number of first names in census×number of last names/number of people surveyed to calculate the number of people in the US with that name.
But that's not a good way. It told me that based off the rarity of my first and last name I was the only one with my name in the US.
But the didn't work because I knew of other people I shared my name with. Because my first name is a family name. I was named for my aunt. My aunt was named for her grandmother. So were my two cousins.
Furthermore, if someone's first name is "Fatima" thier last name is more likely to "Abdallah"than it is to be "Larson" even though "Larson" is a more common last name.
Last, this didn't include name combinations that are specifically avoided. My mid aught example that, if someone's last name was petegrew, they would be less likely to to name there kid "peter" (it was a different time) but for a less frought example, if your last name is Hiltler, you arnt gonna name his "adolf" even if Adolf was the name of your favorite uncle.
Anyways, my point here is that A) do not just assume that all your variables are independent. And B) bad design makes a very strong improvement
In this NBC article about polling before and after Trump’s conviction, I found the following paragraphs that reminded me that hardly anyone actually interprets statistics correctly:
The headline of a Reuters/Ipsos poll released Saturday stated that 1 in 10 Republicans said they are less likely to vote for Trump after the verdict. But a fair warning: Those voters are in the clear minority of their party.
In fact, in the same poll, 55% of Republican voters said the verdict didn’t make a difference to their vote, and 34% said it made them more likely to vote for Trump.
And later:
Make no mistake: Even a sliver of Republicans defecting from Trump could be decisive five months from now. But the major takeaway — right now — is how 9 in 10 Republicans are standing behind him in the Reuters/Ipsos poll.
The problem is, that’s not what the poll says. That 90% of Republicans includes the 55% who said the verdict made no difference. (The 35% is not particularly relevant to my point, because most of those people were honestly probably already set on voting for Trump, and his conviction just added to their enthusiasm, but likely had no meaningful impact on their actual decision.)
What the poll did not bother to do is compare the “who would you vote for today” results and the “did the verdict change your opinion” votes. Of those Republicans who said the verdict made no difference, who was their original candidate of choice? And that’s important, because when I clicked the link to look at the poll itself, 18% of Republicans said that if they were to vote in a head-to-head election today, they would not vote for Trump. The implications of that small percent aside, if 18% of Republicans are not planning to vote for him, and at least some of those Republicans’ opinions were unchanged by the verdict, then that 9 out of 10 includes at least some voters who were never planning to vote for him and still aren’t. Because there is still a small percentage of Republican voters who have not accepted Trump unquestioningly as their demagogue, and their existence should not be ignored in polls. What’s more, when people read polls and statistics about an election, there is at least some support for the argument that their vote (or non-vote) may be affected by their perception of which party is likely to win.
