Language
Language functions such as grammar, vocabulary and literal meaning are typically lateralized to the left hemisphere, especially in right handed individuals.[3] While language production is left-lateralized in up to 90% of right-handers, it is more bilateral, or even right-lateralized, in approximately 50% of left-handers.[4]

Broca's area and Wernicke's area, two areas associated with the production of speech, are located in the left cerebral hemisphere for about 95% of right-handers, but about 70% of left-handers.[5]:69

Auditory and visual processing
The processing of visual and auditory stimuli, spatial manipulation, facial perception, and artistic ability are represented bilaterally.[4] Numerical estimation, comparison and online calculation depend on bilateral parietal regions[6][7] while exact calculation and fact retrieval are associated with left parietal regions, perhaps due to their ties to linguistic processing.[6][7]

...

Depression is linked with a hyperactive right hemisphere, with evidence of selective involvement in "processing negative emotions, pessimistic thoughts and unconstructive thinking styles", as well as vigilance, arousal and self-reflection, and a relatively hypoactive left hemisphere, "specifically involved in processing pleasurable experiences" and "relatively more involved in decision-making processes".

Chaos and Order; the right and left hemispheres: https://orthosphere.wordpress.com/2018/05/23/chaos-and-order-the-right-and-left-hemispheres/
In The Master and His Emissary, Iain McGilchrist writes that a creature like a bird needs two types of consciousness simultaneously. It needs to be able to focus on something specific, such as pecking at food, while it also needs to keep an eye out for predators which requires a more general awareness of environment.

These are quite different activities. The Left Hemisphere (LH) is adapted for a narrow focus. The Right Hemisphere (RH) for the broad. The brains of human beings have the same division of function.

The LH governs the right side of the body, the RH, the left side. With birds, the left eye (RH) looks for predators, the right eye (LH) focuses on food and specifics. Since danger can take many forms and is unpredictable, the RH has to be very open-minded.

The LH is for narrow focus, the explicit, the familiar, the literal, tools, mechanism/machines and the man-made. The broad focus of the RH is necessarily more vague and intuitive and handles the anomalous, novel, metaphorical, the living and organic. The LH is high resolution but narrow, the RH low resolution but broad.

The LH exhibits unrealistic optimism and self-belief. The RH has a tendency towards depression and is much more realistic about a person’s own abilities. LH has trouble following narratives because it has a poor sense of “wholes.” In art it favors flatness, abstract and conceptual art, black and white rather than color, simple geometric shapes and multiple perspectives all shoved together, e.g., cubism. Particularly RH paintings emphasize vistas with great depth of field and thus space and time,[1] emotion, figurative painting and scenes related to the life world. In music, LH likes simple, repetitive rhythms. The RH favors melody, harmony and complex rhythms.

...

Schizophrenia is a disease of extreme LH emphasis. Since empathy is RH and the ability to notice emotional nuance facially, vocally and bodily expressed, schizophrenics tend to be paranoid and are often convinced that the real people they know have been replaced by robotic imposters. This is at least partly because they lose the ability to intuit what other people are thinking and feeling – hence they seem robotic and suspicious.

Oswald Spengler’s The Decline of the West as well as McGilchrist characterize the West as awash in phenomena associated with an extreme LH emphasis. Spengler argues that Western civilization was originally much more RH (to use McGilchrist’s categories) and that all its most significant artistic (in the broadest sense) achievements were triumphs of RH accentuation.

The RH is where novel experiences and the anomalous are processed and where mathematical, and other, problems are solved. The RH is involved with the natural, the unfamiliar, the unique, emotions, the embodied, music, humor, understanding intonation and emotional nuance of speech, the metaphorical, nuance, and social relations. It has very little speech, but the RH is necessary for processing all the nonlinguistic aspects of speaking, including body language. Understanding what someone means by vocal inflection and facial expressions is an intuitive RH process rather than explicit.

...

RH is very much the center of lived experience; of the life world with all its depth and richness. The RH is “the master” from the title of McGilchrist’s book. The LH ought to be no more than the emissary; the valued servant of the RH. However, in the last few centuries, the LH, which has tyrannical tendencies, has tried to become the master. The LH is where the ego is predominantly located. In split brain patients where the LH and the RH are surgically divided (this is done sometimes in the case of epileptic patients) one hand will sometimes fight with the other. In one man’s case, one hand would reach out to hug his wife while the other pushed her away. One hand reached for one shirt, the other another shirt. Or a patient will be driving a car and one hand will try to turn the steering wheel in the opposite direction. In these cases, the “naughty” hand is usually the left hand (RH), while the patient tends to identify herself with the right hand governed by the LH. The two hemispheres have quite different personalities.

The connection between LH and ego can also be seen in the fact that the LH is competitive, contentious, and agonistic. It wants to win. It is the part of you that hates to lose arguments.

Using the metaphor of Chaos and Order, the RH deals with Chaos – the unknown, the unfamiliar, the implicit, the emotional, the dark, danger, mystery. The LH is connected with Order – the known, the familiar, the rule-driven, the explicit, and light of day. Learning something means to take something unfamiliar and making it familiar. Since the RH deals with the novel, it is the problem-solving part. Once understood, the results are dealt with by the LH. When learning a new piece on the piano, the RH is involved. Once mastered, the result becomes a LH affair. The muscle memory developed by repetition is processed by the LH. If errors are made, the activity returns to the RH to figure out what went wrong; the activity is repeated until the correct muscle memory is developed in which case it becomes part of the familiar LH.

Science is an attempt to find Order. It would not be necessary if people lived in an entirely orderly, explicit, known world. The lived context of science implies Chaos. Theories are reductive and simplifying and help to pick out salient features of a phenomenon. They are always partial truths, though some are more partial than others. The alternative to a certain level of reductionism or partialness would be to simply reproduce the world which of course would be both impossible and unproductive. The test for whether a theory is sufficiently non-partial is whether it is fit for purpose and whether it contributes to human flourishing.

...

Analytic philosophers pride themselves on trying to do away with vagueness. To do so, they tend to jettison context which cannot be brought into fine focus. However, in order to understand things and discern their meaning, it is necessary to have the big picture, the overview, as well as the details. There is no point in having details if the subject does not know what they are details of. Such philosophers also tend to leave themselves out of the picture even when what they are thinking about has reflexive implications. John Locke, for instance, tried to banish the RH from reality. All phenomena having to do with subjective experience he deemed unreal and once remarked about metaphors, a RH phenomenon, that they are “perfect cheats.” Analytic philosophers tend to check the logic of the words on the page and not to think about what those words might say about them. The trick is for them to recognize that they and their theories, which exist in minds, are part of reality too.

The RH test for whether someone actually believes something can be found by examining his actions. If he finds that he must regard his own actions as free, and, in order to get along with other people, must also attribute free will to them and treat them as free agents, then he effectively believes in free will – no matter his LH theoretical commitments.

...

We do not know the origin of life. We do not know how or even if consciousness can emerge from matter. We do not know the nature of 96% of the matter of the universe. Clearly all these things exist. They can provide the subject matter of theories but they continue to exist as theorizing ceases or theories change. Not knowing how something is possible is irrelevant to its actual existence. An inability to explain something is ultimately neither here nor there.

If thought begins and ends with the LH, then thinking has no content – content being provided by experience (RH), and skepticism and nihilism ensue. The LH spins its wheels self-referentially, never referring back to experience. Theory assumes such primacy that it will simply outlaw experiences and data inconsistent with it; a profoundly wrong-headed approach.

...

Gödel’s Theorem proves that not everything true can be proven to be true. This means there is an ineradicable role for faith, hope and intuition in every moderately complex human intellectual endeavor. There is no one set of consistent axioms from which all other truths can be derived.

Alan Turing’s proof of the halting problem proves that there is no effective procedure for finding effective procedures. Without a mechanical decision procedure, (LH), when it comes to … [more]

Christian ethics is a branch of Christian theology that defines virtuous behavior and wrong behavior from a Christian perspective. Systematic theological study of Christian ethics is called moral theology, possibly with the name of the respective theological tradition, e.g. Catholic moral theology.

Christian virtues are often divided into four cardinal virtues and three theological virtues. Christian ethics includes questions regarding how the rich should act toward the poor, how women are to be treated, and the morality of war. Christian ethicists, like other ethicists, approach ethics from different frameworks and perspectives. The approach of virtue ethics has also become popular in recent decades, largely due to the work of Alasdair MacIntyre and Stanley Hauerwas.[2]

...

The seven Christian virtues are from two sets of virtues. The four cardinal virtues are Prudence, Justice, Restraint (or Temperance), and Courage (or Fortitude). The cardinal virtues are so called because they are regarded as the basic virtues required for a virtuous life. The three theological virtues, are Faith, Hope, and Love (or Charity).

- Prudence: also described as wisdom, the ability to judge between actions with regard to appropriate actions at a given time
- Justice: also considered as fairness, the most extensive and most important virtue[20]
- Temperance: also known as restraint, the practice of self-control, abstention, and moderation tempering the appetition
- Courage: also termed fortitude, forebearance, strength, endurance, and the ability to confront fear, uncertainty, and intimidation
- Faith: belief in God, and in the truth of His revelation as well as obedience to Him (cf. Rom 1:5:16:26)[21][22]
- Hope: expectation of and desire of receiving; refraining from despair and capability of not giving up. The belief that God will be eternally present in every human's life and never giving up on His love.
- Charity: a supernatural virtue that helps us love God and our neighbors, the same way as we love ourselves.

Seven deadly sins: https://en.wikipedia.org/wiki/Seven_deadly_sins
The seven deadly sins, also known as the capital vices or cardinal sins, is a grouping and classification of vices of Christian origin.[1] Behaviours or habits are classified under this category if they directly give birth to other immoralities.[2] According to the standard list, they are pride, greed, lust, envy, gluttony, wrath, and sloth,[2] which are also contrary to the seven virtues. These sins are often thought to be abuses or excessive versions of one's natural faculties or passions (for example, gluttony abuses one's desire to eat).

originally:
1 Gula (gluttony)
2 Luxuria/Fornicatio (lust, fornication)
3 Avaritia (avarice/greed)
4 Superbia (pride, hubris)
5 Tristitia (sorrow/despair/despondency)
6 Ira (wrath)
7 Vanagloria (vainglory)
8 Acedia (sloth)

Golden Rule: https://en.wikipedia.org/wiki/Golden_Rule
The Golden Rule (which can be considered a law of reciprocity in some religions) is the principle of treating others as one would wish to be treated. It is a maxim that is found in many religions and cultures.[1][2] The maxim may appear as _either a positive or negative injunction_ governing conduct:

- One should treat others as one would like others to treat oneself (positive or directive form).[1]
- One should not treat others in ways that one would not like to be treated (negative or prohibitive form).[1]
- What you wish upon others, you wish upon yourself (empathic or responsive form).[1]
The Golden Rule _differs from the maxim of reciprocity captured in do ut des—"I give so that you will give in return"—and is rather a unilateral moral commitment to the well-being of the other without the expectation of anything in return_.[3]

The concept occurs in some form in nearly every religion[4][5] and ethical tradition[6] and is often considered _the central tenet of Christian ethics_[7] [8]. It can also be explained from the perspectives of psychology, philosophy, sociology, human evolution, and economics. Psychologically, it involves a person empathizing with others. Philosophically, it involves a person perceiving their neighbor also as "I" or "self".[9] Sociologically, "love your neighbor as yourself" is applicable between individuals, between groups, and also between individuals and groups. In evolution, "reciprocal altruism" is seen as a distinctive advance in the capacity of human groups to survive and reproduce, as their exceptional brains demanded exceptionally long childhoods and ongoing provision and protection even beyond that of the immediate family.[10] In economics, Richard Swift, referring to ideas from David Graeber, suggests that "without some kind of reciprocity society would no longer be able to exist."[11]

...

hmm, Meta-Golden Rule already stated:
Seneca the Younger (c. 4 BC–65 AD), a practitioner of Stoicism (c. 300 BC–200 AD) expressed the Golden Rule in his essay regarding the treatment of slaves: "Treat your inferior as you would wish your superior to treat you."[23]

...

The "Golden Rule" was given by Jesus of Nazareth, who used it to summarize the Torah: "Do to others what you want them to do to you." and "This is the meaning of the law of Moses and the teaching of the prophets"[33] (Matthew 7:12 NCV, see also Luke 6:31). The common English phrasing is "Do unto others as you would have them do unto you". A similar form of the phrase appeared in a Catholic catechism around 1567 (certainly in the reprint of 1583).[34] The Golden Rule is _stated positively numerous times in the Hebrew Pentateuch_ as well as the Prophets and Writings. Leviticus 19:18 ("Forget about the wrong things people do to you, and do not try to get even. Love your neighbor as you love yourself."; see also Great Commandment) and Leviticus 19:34 ("But treat them just as you treat your own citizens. Love foreigners as you love yourselves, because you were foreigners one time in Egypt. I am the Lord your God.").

The Old Testament Deuterocanonical books of Tobit and Sirach, accepted as part of the Scriptural canon by Catholic Church, Eastern Orthodoxy, and the Non-Chalcedonian Churches, express a _negative form_ of the golden rule:

"Do to no one what you yourself dislike."

— Tobit 4:15
"Recognize that your neighbor feels as you do, and keep in mind your own dislikes."

— Sirach 31:15
Two passages in the New Testament quote Jesus of Nazareth espousing the _positive form_ of the Golden rule:

Matthew 7:12
Do to others what you want them to do to you. This is the meaning of the law of Moses and the teaching of the prophets.

Luke 6:31
Do to others what you would want them to do to you.

...

The passage in the book of Luke then continues with Jesus answering the question, "Who is my neighbor?", by telling the parable of the Good Samaritan, indicating that "your neighbor" is anyone in need.[35] This extends to all, including those who are generally considered hostile.

Jesus' teaching goes beyond the negative formulation of not doing what one would not like done to themselves, to the positive formulation of actively doing good to another that, if the situations were reversed, one would desire that the other would do for them. This formulation, as indicated in the parable of the Good Samaritan, emphasizes the needs for positive action that brings benefit to another, not simply restraining oneself from negative activities that hurt another. Taken as a rule of judgment, both formulations of the golden rule, the negative and positive, are equally applicable.[36]

The Golden Rule: Not So Golden Anymore: https://philosophynow.org/issues/74/The_Golden_Rule_Not_So_Golden_Anymore
Pluralism is the most serious problem facing liberal democracies today. We can no longer ignore the fact that cultures around the world are not simply different from one another, but profoundly so; and the most urgent area in which this realization faces us is in the realm of morality. Western democratic systems depend on there being at least a minimal consensus concerning national values, especially in regard to such things as justice, equality and human rights. But global communication, economics and the migration of populations have placed new strains on Western democracies. Suddenly we find we must adjust to peoples whose suppositions about the ultimate values and goals of life are very different from ours. A clear lesson from events such as 9/11 is that disregarding these differences is not an option. Collisions between worldviews and value systems can be cataclysmic. Somehow we must learn to manage this new situation.

For a long time, liberal democratic optimism in the West has been shored up by suppositions about other cultures and their differences from us. The cornerpiece of this optimism has been the assumption that whatever differences exist they cannot be too great. A core of ‘basic humanity’ surely must tie all of the world’s moral systems together – and if only we could locate this core we might be able to forge agreements and alliances among groups that otherwise appear profoundly opposed. We could perhaps then shelve our cultural or ideological differences and get on with the more pleasant and productive business of celebrating our core agreement. One cannot fail to see how this hope is repeated in order buoy optimism about the Middle East peace process, for example.

...

It becomes obvious immediately that no matter how widespread we want the Golden Rule to be, there are some ethical systems that we have to admit do not have it. In fact, there are a few traditions that actually disdain the Rule. In philosophy, the Nietzschean tradition holds that the virtues implicit in the Golden Rule are antithetical to the true virtues of self-assertion and the will-to-power. Among religions, there are a good many that prefer to emphasize the importance of self, cult, clan or tribe rather than of general others; and a good many other religions for whom large populations are simply excluded from goodwill, being labeled as outsiders, heretics or … [more]

https://en.wikipedia.org/wiki/Trust_(emotion)
https://en.wikipedia.org/wiki/Envy
https://en.wikipedia.org/wiki/Guilt_(emotion)

I think I follow pretty smart people but I see these small sample studies on my timeline all the time. Remember people, the law of large numbers is a true theorem but the law of small numbers is a joke by Tversky & Kahneman:

There are few recorded population numbers for the whole of antiquity, and those that exist are often rhetorical or symbolic. Unlike the contemporaneous Han Dynasty, no general census survives for the Roman Empire. The late period of the Roman Republic provides a small exception to this general rule: serial statistics for Roman citizen numbers, taken from census returns, survive for the early Republic through the 1st century CE.[41] Only the figures for periods after the mid-3rd century BCE are reliable, however. Fourteen figures are available for the 2nd century BCE (from 258,318 to 394,736). Only four figures are available for the 1st century BCE, and are feature a large break between 70/69 BCE (910,000) and 28 BCE (4,063,000). The interpretation of the later figures—the Augustan censuses of 28 BCE, 8 BCE, and 14 CE—is therefore controversial.[42] Alternate interpretations of the Augustan censuses (such as those of E. Lo Cascio[43]) produce divergent population histories across the whole imperial period.[44]

Roman population size: the logic of the debate: https://www.princeton.edu/~pswpc/pdfs/scheidel/070706.pdf
- Walter Scheidel (cited in book by Vaclav Smil, "Why America is Not a New Rome")

Our ignorance of ancient population numbers is one of the biggest obstacles to our understanding of Roman history. After generations of prolific scholarship, we still do not know how many people inhabited Roman Italy and the Mediterranean at any given point in time. When I say ‘we do not know’ I do not simply mean that we lack numbers that are both precise and safely known to be accurate: that would surely be an unreasonably high standard to apply to any pre-modern society. What I mean is that even the appropriate order of magnitude remains a matter of intense dispute.

Historical urban community sizes: https://en.wikipedia.org/wiki/Historical_urban_community_sizes

World population estimates: https://en.wikipedia.org/wiki/World_population_estimates
As a general rule, the confidence of estimates on historical world population decreases for the more distant past. Robust population data only exists for the last two or three centuries. Until the late 18th century, few governments had ever performed an accurate census. In many early attempts, such as in Ancient Egypt and the Persian Empire, the focus was on counting merely a subset of the population for purposes of taxation or military service.[3] Published estimates for the 1st century ("AD 1") suggest an uncertainty of the order of 50% (estimates range between 150 and 330 million). Some estimates extend their timeline into deep prehistory, to "10,000 BC", i.e. the early Holocene, when world population estimates range roughly between one and ten million (with an uncertainty of up to an order of magnitude).[4][5]

Estimates for yet deeper prehistory, into the Paleolithic, are of a different nature. At this time human populations consisted entirely of non-sedentary hunter-gatherer populations, with anatomically modern humans existing alongside archaic human varieties, some of which are still ancestral to the modern human population due to interbreeding with modern humans during the Upper Paleolithic. Estimates of the size of these populations are a topic of paleoanthropology. A late human population bottleneck is postulated by some scholars at approximately 70,000 years ago, during the Toba catastrophe, when Homo sapiens population may have dropped to as low as between 1,000 and 10,000 individuals.[6][7] For the time of speciation of Homo sapiens, some 200,000 years ago, an effective population size of the order of 10,000 to 30,000 individuals has been estimated, with an actual "census population" of early Homo sapiens of roughly 100,000 to 300,000 individuals.[8]

Topics
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design

The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics: https://www.aeaweb.org/articles?id=10.1257/jep.24.2.3

On data, experiments, incentives and highly unconvincing research – papers and hot beverages: https://papersandhotbeverages.wordpress.com/2015/10/31/on-data-experiments-incentives-and-highly-unconvincing-research/
In my view, it has just to do with the fact that academia is a peer monitored organization. In the case of (bad) data collection papers, issues related to measurement are typically boring. They are relegated to appendices, no one really has an incentive to monitor it seriously. The problem is similar in formal theory: no one really goes through the algebra in detail, but it is in principle feasible to do it, and, actually, sometimes these errors are detected. If discussing the algebra of a proof is almost unthinkable in a seminar, going into the details of data collection, measurement and aggregation is not only hard to imagine, but probably intrinsically infeasible.

Something different happens for the experimentalist people. As I was saying, I feel we have come to a point in which many papers are evaluated based on the cleverness and originality of the research design (“Using the World Cup qualifiers as an instrument for patriotism!? Woaw! how cool/crazy is that! I wish I had had that idea”). The sexiness of the identification strategy has too often become a goal in itself. When your peers monitor you paying more attention to the originality of the identification strategy than to the research question, you probably have an incentive to mine reality for ever crazier discontinuities. It is true methodologists have been criticized in the past for analogous reasons, such as being guided by the desire to increase mathematical complexity without a clear benefit. But, if you work with pure formal theory or statistical theory, your work is not meant to immediately answer question about the real world, but instead to serve other researchers in their quest. This is something that can, in general, not be said of applied CI work.

https://twitter.com/pseudoerasmus/status/662007951415238656
This post should have been entitled “Zombies who only think of their next cool IV fix”
https://twitter.com/pseudoerasmus/status/662692917069422592
massive lust for quasi-natural experiments, regression discontinuities
barely matters if the effects are not all that big
I suppose even the best of things must reach their decadent phase; methodological innov. to manias……

https://twitter.com/cblatts/status/920988530788130816
Following this "collapse of small-N social psych results" business, where do I predict econ will collapse? I see two main contenders.
One is lab studies. I dallied with these a few years ago in a Kenya lab. We ran several pilots of N=200 to figure out the best way to treat
and to measure the outcome. Every pilot gave us a different stat sig result. I could have written six papers concluding different things.
I gave up more skeptical of these lab studies than ever before. The second contender is the long run impacts literature in economic history
We should be very suspicious since we never see a paper showing that a historical event had no effect on modern day institutions or dvpt.
On the one hand I find these studies fun, fascinating, and probably true in a broad sense. They usually reinforce a widely believed history
argument with interesting data and a cute empirical strategy. But I don't think anyone believes the standard errors. There's probably a HUGE
problem of nonsignificant results staying in the file drawer. Also, there are probably data problems that don't get revealed, as we see with
the recent Piketty paper (http://marginalrevolution.com/marginalrevolution/2017/10/pikettys-data-reliable.html). So I take that literature with a vat of salt, even if I enjoy and admire the works
I used to think field experiments would show little consistency in results across place. That external validity concerns would be fatal.
In fact the results across different samples and places have proven surprisingly similar across places, and added a lot to general theory
Last, I've come to believe there is no such thing as a useful instrumental variable. The ones that actually meet the exclusion restriction
are so weird & particular that the local treatment effect is likely far different from the average treatment effect in non-transparent ways.
Most of the other IVs don't plausibly meet the e clue ion restriction. I mean, we should be concerned when the IV estimate is always 10x
larger than the OLS coefficient. This I find myself much more persuaded by simple natural experiments that use OLS, diff in diff, or
discontinuities, alongside randomized trials.

What do others think are the cliffs in economics?
PS All of these apply to political science too. Though I have a special extra target in poli sci: survey experiments! A few are good. I like
Dan Corstange's work. But it feels like 60% of dissertations these days are experiments buried in a survey instrument that measure small
changes in response. These at least have large N. But these are just uncontrolled labs, with negligible external validity in my mind.
The good ones are good. This method has its uses. But it's being way over-applied. More people have to make big and risky investments in big
natural and field experiments. Time to raise expectations and ambitions. This expectation bar, not technical ability, is the big advantage
economists have over political scientists when they compete in the same space.
(Ok. So are there any friends and colleagues I haven't insulted this morning? Let me know and I'll try my best to fix it with a screed)

HOW MUCH SHOULD WE TRUST DIFFERENCES-IN-DIFFERENCES ESTIMATES?∗: https://economics.mit.edu/files/750
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the auto-correlation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre” and “post” period and explicitly takes into account the effective sample size works well even for small numbers of states.

‘METRICS MONDAY: 2SLS–CHRONICLE OF A DEATH FORETOLD: http://marcfbellemare.com/wordpress/12733
As it turns out, Young finds that
1. Conventional tests tend to overreject the null hypothesis that the 2SLS coefficient is equal to zero.
2. 2SLS estimates are falsely declared significant one third to one half of the time, depending on the method used for bootstrapping.
3. The 99-percent confidence intervals (CIs) of those 2SLS estimates include the OLS point estimate over 90 of the time. They include the full OLS 99-percent CI over 75 percent of the time.
4. 2SLS estimates are extremely sensitive to outliers. Removing simply one outlying cluster or observation, almost half of 2SLS results become insignificant. Things get worse when removing two outlying clusters or observations, as over 60 percent of 2SLS results then become insignificant.
5. Using a Durbin-Wu-Hausman test, less than 15 percent of regressions can reject the null that OLS estimates are unbiased at the 1-percent level.
6. 2SLS has considerably higher mean squared error than OLS.
7. In one third to one half of published results, the null that the IVs are totally irrelevant cannot be rejected, and so the correlation between the endogenous variable(s) and the IVs is due to finite sample correlation between them.
8. Finally, fewer than 10 percent of 2SLS estimates reject instrument irrelevance and the absence of OLS bias at the 1-percent level using a Durbin-Wu-Hausman test. It gets much worse–fewer than 5 percent–if you add in the requirement that the 2SLS CI that excludes the OLS estimate.

Methods Matter: P-Hacking and Causal Inference in Economics*: http://ftp.iza.org/dp11796.pdf
Applying multiple methods to 13,440 hypothesis tests reported in 25 top economics journals in 2015, we show that selective publication and p-hacking is a substantial problem in research employing DID and (in particular) IV. RCT and RDD are much less problematic. Almost 25% of claims of marginally significant results in IV papers are misleading.

https://twitter.com/NoamJStein/status/1040887307568664577
Ever since I learned social science is completely fake, I've had a lot more time to do stuff that matters, like deadlifting and reading about Mediterranean haplogroups
--
Wait, so, from fakest to realest IV>DD>RCT>RDD? That totally matches my impression.

https://twitter.com/wwwojtekk/status/1190731344336293889
https://archive.is/EZu0h
Great (not completely new but still good to have it in one place) discussion of RCTs and inference in economics by Deaton, my favorite sentences (more general than just about RCT) below
Randomization in the tropics revisited: a theme and eleven variations: https://scholar.princeton.edu/sites/default/files/deaton/files/deaton_randomization_revisited_v3_2019.pdf

Hubris (/ˈhjuːbrɪs/, also hybris, from ancient Greek ὕβρις) describes a personality quality of extreme or foolish pride or dangerous overconfidence.[1] In its ancient Greek context, it typically describes behavior that defies the norms of behavior or challenges the gods, and which in turn brings about the downfall, or nemesis, of the perpetrator of hubris.

...

In ancient Greek, hubris referred to actions that shamed and humiliated the victim for the pleasure or gratification of the abuser.[3] The term had a strong sexual connotation, and the shame reflected upon the perpetrator as well.[4]

Violations of the law against hubris included what might today be termed assault and battery; sexual crimes; or the theft of public or sacred property. Two well-known cases are found in the speeches of Demosthenes, a prominent statesman and orator in ancient Greece. These two examples occurred when first Midias punched Demosthenes in the face in the theatre (Against Midias), and second when (in Against Conon) a defendant allegedly assaulted a man and crowed over the victim. Yet another example of hubris appears in Aeschines' Against Timarchus, where the defendant, Timarchus, is accused of breaking the law of hubris by submitting himself to prostitution and anal intercourse. Aeschines brought this suit against Timarchus to bar him from the rights of political office and his case succeeded.[5]

In ancient Athens, hubris was defined as the use of violence to shame the victim (this sense of hubris could also characterize rape[6]). Aristotle defined hubris as shaming the victim, not because of anything that happened to the committer or might happen to the committer, but merely for that committer's own gratification:

to cause shame to the victim, not in order that anything may happen to you, nor because anything has happened to you, but merely for your own gratification. Hubris is not the requital of past injuries; this is revenge. As for the pleasure in hubris, its cause is this: naive men think that by ill-treating others they make their own superiority the greater.[7][8][9]

Crucial to this definition are the ancient Greek concepts of honour (τιμή, timē) and shame (αἰδώς, aidōs). The concept of honour included not only the exaltation of the one receiving honour, but also the shaming of the one overcome by the act of hubris. This concept of honour is akin to a zero-sum game. Rush Rehm simplifies this definition of hubris to the contemporary concept of "insolence, contempt, and excessive violence".[citation needed]

...

In its modern usage, hubris denotes overconfident pride combined with arrogance.[10] Hubris is often associated with a lack of humility. Sometimes a person's hubris is also associated with ignorance. The accusation of hubris often implies that suffering or punishment will follow, similar to the occasional pairing of hubris and nemesis in Greek mythology. The proverb "pride goeth (goes) before destruction, a haughty spirit before a fall" (from the biblical Book of Proverbs, 16:18) is thought to sum up the modern use of hubris. Hubris is also referred to as "pride that blinds" because it often causes a committer of hubris to act in foolish ways that belie common sense.[11] In other words, the modern definition may be thought of as, "that pride that goes just before the fall."

Examples of hubris are often found in literature, most famously in John Milton's Paradise Lost, 'where Lucifer attempts to force the other angels to worship him, but is cast into hell by God and the innocent angels, and proclaims: "Better to reign in hell than serve in heaven." Victor in Mary Shelley's Frankenstein manifests hubris in his attempt to become a great scientist by creating life through technological means, but eventually regrets this previous desire. Marlowe's play Doctor Faustus portrays the eponymous character as a scholar whose arrogance and pride compel him to sign a deal with the Devil, and retain his haughtiness until his death and damnation, despite the fact that he could easily have repented had he chosen to do so.

One notable example is the Battle of Little Big Horn, as General George Armstrong Custer was apocryphally reputed to have said there: "Where did all those damned Indians come from?"[12]

The confidence interval is the answer to the request: "Give me an interval that will bracket the true value of the parameter in 100p% of the instances of an experiment that is repeated a large number of times." The credible interval is an answer to the request: "Give me an interval that brackets the true value with probability pp given the particular sample I've actually observed." To be able to answer the latter request, we must first adopt either (a) a new concept of the data generating process or (b) a different concept of the definition of probability itself.

http://stats.stackexchange.com/questions/139290/a-psychology-journal-banned-p-values-and-confidence-intervals-is-it-indeed-wise

PS. Note that my question is not about the ban itself; it is about the suggested approach. I am not asking about frequentist vs. Bayesian inference either. The Editorial is pretty negative about Bayesian methods too; so it is essentially about using statistics vs. not using statistics at all.

wut

http://stats.stackexchange.com/questions/6966/why-continue-to-teach-and-use-hypothesis-testing-when-confidence-intervals-are
http://stats.stackexchange.com/questions/2356/are-there-any-examples-where-bayesian-credible-intervals-are-obviously-inferior
http://stats.stackexchange.com/questions/2272/whats-the-difference-between-a-confidence-interval-and-a-credible-interval
http://stats.stackexchange.com/questions/6652/what-precisely-is-a-confidence-interval
http://stats.stackexchange.com/questions/1164/why-havent-robust-and-resistant-statistics-replaced-classical-techniques/
http://stats.stackexchange.com/questions/16312/what-is-the-difference-between-confidence-intervals-and-hypothesis-testing
http://stats.stackexchange.com/questions/31679/what-is-the-connection-between-credible-regions-and-bayesian-hypothesis-tests
http://stats.stackexchange.com/questions/11609/clarification-on-interpreting-confidence-intervals
http://stats.stackexchange.com/questions/16493/difference-between-confidence-intervals-and-prediction-intervals

- "Bonferroni approach" is just union bound
- so Pr(|hat p_i - p_i| > ε for any i) <= 2k e^{-ε^2 n} = δ
- ε = sqrt(ln(2k/δ)/n)
- Bonferroni approach should work for case of any dependent Bernoulli r.v.s

The advantages of conformal prediction are many fold. These ideas assume very little about the thing you are trying to forecast, the tool you’re using to forecast or how the world works, and they still produce a pretty good confidence interval. Even if you’re an unrepentant Bayesian, using some of the machinery of conformal prediction, you can tell when things have gone wrong with your prior. The learners work online, and with some modifications and considerations, with batch learning. One of the nice things about calculating confidence intervals as a part of your learning process is they can actually lower error rates or use in semi-supervised learning as well. Honestly, I think this is the best bag of tricks since boosting; everyone should know about and use these ideas.

The essential idea is that a “conformity function” exists. Effectively you are constructing a sort of multivariate cumulative distribution function for your machine learning gizmo using the conformity function. Such CDFs exist for classical stuff like ARIMA and linear regression under the correct circumstances; CP brings the idea to machine learning in general, and to models like ARIMA when the standard parametric confidence intervals won’t work. Within the framework, the conformity function, whatever may be, when used correctly can be guaranteed to give confidence intervals to within a probabilistic tolerance. The original proofs and treatments of conformal prediction, defined for sequences, is extremely computationally inefficient. The conditions can be relaxed in many cases, and the conformity function is in principle arbitrary, though good ones will produce narrower confidence regions. Somewhat confusingly, these good conformity functions are referred to as “efficient” -though they may not be computationally efficient.

hmm:
https://twitter.com/davidshor/status/888441370247090176
https://archive.is/BiPxf
Friends don't let friends plot 95% confidence intervals

There is a 98.3% chance that b>a, even through their 95% oonfsdenoe intervals overlap