Long Island Homestead, Study from Nature, 1859 by Andrew W. Warren
British soldiers, during the Battle of Long Island in 1776, were astonished at the plenitude of American farms in this demi-Eden.
Sunday, January 3, 2021
Saturday, January 2, 2021
Dadgummit!
My two best traits are handicaps? Guess its lucky I am already married.
From Exceptional intelligence and easygoingness may hurt your prospects: Threshold effects for rated mate characteristics by Gilles E. Gignac and Clare L. Starbuck. From the Abstract:
Prospective mate characteristics such as kindness, intelligence, easygoingness, and physical attraction are ranked consistently highly by both men and women. However, rank measurement does not allow for determinations of what level of a mate characteristic is rated most desirable. Based on a more informative percentile scale measurement approach, it was reported recently that mean desirability ratings of IQ in a prospective partner peaked at the 90th percentile, with a statistically significant reduction from the 90th to the 99th percentiles. The purpose of this investigation was to replicate the recently reported non‐linear desirability effect associated with IQ, in addition to the evaluation of three other valued mate characteristics: easygoing, kindness, and physical attraction. Based on a sample of 214 young adults, it was found that all four mate characteristics peaked at the 90th percentile. However, the IQ and easygoing mean desirability ratings evidenced statistically significant mean reductions across the 90th to the 99th percentiles, whereas kindness and physical attraction did not. Finally, the objectively and subjectively assessed intelligence of the participants was not found to be associated with the participants’ desirability ratings of IQ. We interpreted the results to be consistent with a broadly conceptualized threshold hypothesis, which states that the perceived benefits of valued mate characteristics may not extend beyond a certain point. However, mate characteristics such as intelligence and easygoing become somewhat less attractive at very elevated levels, at least based on preference ratings, for reasons that may be biological and/or psycho‐social in nature.
Publication bias deranges the scientific literature
From Science Fictions by Stuart Ritchie. Page 92. An excellent discussion of the impact of publication bias in undermining meta-analysis and potentially driving false conclusions.
In the context of publication bias, what we’re interested in is how the effect size and the sample size relate to one another. If you plot one versus the other, with one dot per study, you’d expect your graph to look something like Figure 2A below. (Note that this is an idealised version of a meta-analysis; real datasets almost never look this clear-cut.) Looking at this ‘funnel plot’ (so named for what are hopefully obvious reasons), you can see how all the smaller studies, towards the bottom of the y-axis, fluctuate widely; as you progress up the y-axis, to bigger studies, the dots begin to cluster around the average effect, illustrating what we’ve just discussed about larger studies being more precise. The variation on the x-axis is why it’s a bad idea to take for granted the effect from any individual study: even though there is a real effect in this example, individual studies have under- and over-shot its ‘true’ size by varying degrees (though the biggest studies do an admirable job). In any case, nothing seems to be missing here: the upside-down funnel shape is just what we’d expect if all the studies had converged upon a real effect.
<s>Click to enlarge.
Figure 2. Funnel plots from an imaginary meta-analysis, in two different scenarios. In scenario A, the distribution of the thirty studies is about what you’d expect if every study ever done on the topic had been published. In scenario B, the six studies from the bottom-left section (studies with small samples and small effects) are missing – a pattern that might signal publication bias. The vertical line in the middle of each graph is the overall effect size calculated by each meta-analysis. In the case of scenario B, it’s been shifted to the right, meaning that the meta-analysis is coming up with a bigger effect than it should.</s>
Just as in an archaeological dig, where the absence of particular objects tells you interesting things about the historical people you’re investigating – for instance, a lack of weapons might mean they were civilians rather than soldiers – we can learn a lot from what we don’t see in a meta-analysis. What if our plot looks more like Figure 2B? Here, we’ve lost a chunk from the expected shape. The studies we’d expect to see in the lower left of our, which had small sample sizes as well as small effects, are missing. Thinking like an archaeologist, a meta-analyst might infer that those studies were done, yet instead of being published, were file-drawered. Why? A likely explanation is that these small-sample, small-effect studies had p-values higher than 0.05 and were dismissed as unimportant nulls.
Perhaps the scientists who ran these studies thought something like: ‘well, it was only a small study, and the small effect I found is probably just due to noisy data. Come to think of it, I was silly even to expect to find an effect here! There’s no point in trying to publish this.’ Crucially, though, this post hoc rationalisation wouldn’t have occurred to them if the same small-sample study, with its potentially noisy data, happened to show a large effect: they’d have eagerly sent off their positive results to a journal. This double standard, based on the entrenched human tendency towards confirmation bias (interpreting evidence in the way that fits our pre-existing beliefs and desires), is what’s at the root of publication bias.
If you consider the overall conclusions of a meta-analysis based on Figure 2B rather than 2A, you can see how publication bias deranges the scientific literature. If the studies with small effects have been removed from the funnel shape, the overall effect that shows up in the meta-analysis will by definition be larger than is justified. We get an exaggerated view of the importance of the effects and can be misled into believing something exists when it doesn’t. By failing to publish null or ambiguous studies, researchers force blinkers onto anyone who reads the scientific literature.
The Critical Theory Authoritarian Mandarin Class
A great example of the unexamined assumptions of the Mandarin Class faux Platonic Philospher King wannabes. From 2020 Taught Us How to Fix This by David Brooks. Painfully obviously, 2020 did not teach us how to fix this and Brooks takes a long while to get around to describing what "this" is that needs fixing. He opens with:
This is the year that broke the truth. This is the year when millions of Americans — and not just your political opponents — seemed impervious to evidence, willing to believe the most outlandish things if it suited their biases, and eager to develop fervid animosities based on crude stereotypes.
The question is, who are these people who are impervious to evidence, believing in outlandish things which appeal to their biases and eager to develop fervid animosities based on crude stereotypes. Sure sounds like the critical theory left who conduct the politics of personal destruction, deplatform anybody who disagrees, are happy to suppress any diversity of opinion, still entertain the Russian conspiracy as a viable theory of the facts, disbelieve in heritability of various traits, and still refuse to accept that there is any variability between individuals and therefore any differential in outcomes must be system bias rather than variation on capabilities, behaviors, and objectives.
Since it is David "Critical Theory Establishment Class" Brooks, it seems pretty obvious he is not talking about them, but the indictment doesn't really match anyone else. Talk about willful blindness.
He is retailing long discarded myths.
So many of our hopes are based on the idea that the key to change is education.
We have known for years that persuasion is much more than simple education and presentation of facts. All the way back some 2,500 years ago when we formalized rhetoric as the means to persuade. Nobody paying attention had hopes based on simple education or fact presentation.
But this was the year that showed that our models for how we change minds or change behavior are deeply flawed.
It turns out that if you tell someone their facts are wrong, you don’t usually win them over; you just entrench false belief.
Again, how unaware is Brooks to even advance this as being true? And are the beliefs really false? If, at the beginning of the pandemic you had a firm conviction that masking was an integral part of slowing the spread of the disease or, alternatively, you had a passionated conviction that masking was empirically irrelevant, both positions have been supported by the same experts at different points in the past nine months. We have to at least get to some sort of relevant and credentialed claim which is widely supported and for which there is little contra-evidence before we can start talking glibly about false beliefs.
Brooks then goes after the failure of racial diversity training to make any empirical difference in behaviors and outcomes. Given the comprehensive and repeated failure to demonstrate any positive or useful outcome to diversity training, one might question the assumption that there is a real problem to be solved. Not Brooks.
Implicit bias is absolutely real. The problem is that courses to reduce its effects don’t seem to work.
Well . . . possibly. But if you keep taking a treatment for a disease and nothing happens, perhaps the problem is not the treatment but that the disease, as diagnosed, isn't real.
There is plenty of evidence that people have varied objectives, life goals, assessments of facts and weighting of factors, differential risk assumptions, different diagnoses of causersal mechanisms, different trade-off decisions, different formulations of desirable solutions sets, etc. Of course people will make different decisions about whom they wish to work to solve a problem. People want to collaborate with like minded individuals.
The Mandarin Class and Critical Theory devotees really want America to be a racist nation and the America of liberty and human universalism and enlightenment values keeps refuting them. Keeps refuting Brooks. Diversity training doesn't work because it is solving the wrong problem. Solving an imaginary problem.
People aren't biased owing to race per se, they are biased based on epistemic variances.
The cognitive dissonance outs the authoritarianism of the Brooks of the world.
Finally, our training model of “teaching people to be good” is based on the illusion that you can change people’s minds and behaviors by presenting them with new information and new thoughts. If this were generally so, moral philosophers would behave better than the rest of us. They don’t.
People change when they are put in new environments, in permanent relationship with diverse groups of people. Their embodied minds adapt to the environments in a million different ways we will never understand or be able to plan. Decades ago, the social psychologist Gordon Allport wrote about the contact hypothesis, that doing life together with people of other groups can reduce prejudice and change minds. It’s how new emotional bonds are formed, how new conceptions of who is “us” and who is “them” come into being.
The superficial way to change minds and behavior doesn’t seem to work, to bridge either racial, partisan or class lines. Real change seems to involve putting bodies from different groups in the same room, on the same team and in the same neighborhood. That’s national service programs. That’s residential integration programs across all lines of difference. That’s workplace diversity, equity and inclusion — permanent physical integration, not training.
This points to a more fundamental vision of social change, but it is a hard-won lesson from a bitterly divisive year.
Regardless of the huge diversity of goals and values and behaviors and abilities, etc., Brooks wants to abandon empiricism, abandon human universalism, abandon personal freedom and force a racist model where the Mandarin Class can force citizens to serve at the will of the authoritarian dictatorship to solve a problem which does not exist.
Brooks wants to put "bodies from different groups in the same room, on the same team and in the same neighborhood" without realizing that that happens all the time among free people pursuing shared goals. Americans work across racial, ethnic, gender, religious, class lines where it is mutually beneficial. And we do it well and are pleased when it is successful.
It is only the Critical Theory Mandarin Class who are upset when this happens based on free people making free choices.
An Insight
There are two ways for Europe to meet this emissions goal:
— Steve Milloy (@JunkScience) December 11, 2020
1. Destroy the standard of living in Europe; or
2. Lie about meeting the goal.
I wonder which European politicians will choose.https://t.co/i33gSnvAKe
I see wonderful things
When I was an unhappy college student in Delhi, I used to ride a bus 90 mins each way to sneak into the library of another university (JNU) to read @ScienceMagazine. Kind of cool having a paper there now. :-) https://t.co/p5Vr7aEc8l
— Gautam Rao (@raogautam) December 10, 2020
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