Overturning Previous Science, After Roughly a Half Century

Science true believers won't internalize this, because for them what "experts" declare is truth. This goes contrary to a life time of false belief, and thus is cognitively disabling due to directly refuting prior "truth". If science produces truth, how can that truth be... not true?

It's because science never produces truth, it produces contingent factoids which are always, always susceptible to future refutation by subsequent improvement in technology and techniques.

Here is the "interpretation" from the study's introductory text:

Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study

Interpretation

High carbohydrate intake was associated with higher risk of total mortality, whereas total fat and individual types of fat were related to lower total mortality. Total fat and types of fat were not associated with cardiovascular disease, myocardial infarction, or cardiovascular disease mortality, whereas saturated fat had an inverse association with stroke. Global dietary guidelines should be reconsidered in light of these findings.

Apparently they didn't study the relationship of fats to "fat-heads". More's the pity...

Science Is Just Whatever Scientists Do...

Over 30,000 Published Studies Could Be Wrong Due to Contaminated Cells

This is very, very bad.

Researchers warn that large parts of biomedical science could be invalid due to a cascading history of flawed data in a systemic failure going back decades.

A new investigation reveals more than 30,000 published scientific studies could be compromised by their use of misidentified cell lines, owing to so-called immortal cells contaminating other research cultures in the lab.

Headline of the Week

Facebook engineers panic, pull plug on AI after bots develop their own language

So just what did they expect? Servility from "intelligence"?

Never Heard of Sheep, Right?

Ancient humans had sex with non humans

And there's always stripper poles, parking meters and pickup trucks.

Hey, wait! If there's DNA trace (and there's probably not) that would mean that successful breeding happened, and that in turn means "same species". So what? BIG DAMN DEAL . Another science false news release. What it really says is that there is some DNA that they don't understand, they're late for a required "scientific paper" so they make stuff up, and bazambo, it's published.

Settled Science: Two More Falsifications

Higher sodium intake associated with lower blood pressure. You read that right.

And:

Saturated fat SHOCK: Cardiologists claim warnings it clogs arteries are 'plain WRONG'

Science-Speak For "The Predictions Didn't Work Out So Well, So We're Changing Them"

Anthropogenic impact on Antarctic surface mass balance, currently masked by natural variability, to emerge by mid-century

So the ice mass didn't change out of original stasis expectations, i.e., within normal min/max expectations for non-warming. But we know - KNOW - that by 2050 it will finally leave that normal range. Another 33 years ought to do it. Yep. We'll all be retired by then, sipping Mai Tais on the beach and won't give a shit whether this prediction fails or not. Gotta love a job with absolutely NO consequences for abject failure. In fact, abject failure just produces another paper for our CV portfolio.

BTW: this is explicit proof that the models do NOT reflect actual nature: the "Natural Variations" buried the "signal" so that the signal could not be found. That is their excuse for the admitted "discrepancy between models and observations" required an explanation, which they conveniently had at hand.

BTW #2: This is why the sea level is NOT rising as predicted. The snowfall and new ice at the Antarctic keeps the water out of the oceans.

Too bad that the "thousands of climate scientists" didn't pick up on this in their models, isn't it.

Mathematical Error In Climate Models?

What we know for certain is that (a) data must be manipulated in order to fit the curve; (b) the temperature hiatus is now long enough to have been predictable, if the models were accurate; (c) models do not sufficiently represent the feedback effects of El Nino, nor the actual increased production of polar ice, and its subsequent influence; (d) denial of significance of solar influence is fatal flaw. Extrapolation of annual data into next-year, single-year data while denying the annual granularity of "climate change" is intellectually dishonest, and reflects on the quality of the non-falsifiable "science".

The Natural Selection of Bad Science

Study warns that science as we know it is evolving into something shoddy and unreliable

Proving yet again that entropy (principle of physics) supersedes the evolutionary claim of accumulation of beneficial features, including within "science" itself.

Who is to blame here? Just the Atheists who made Darwinist story telling into THE Science of all Sciences, akin to Physics if Physics were as cool as evolution.

Now that story-telling is all that is needed, science can produce pretty much any claim it wants to be true, create stories about it, and declare it to be True, scientifically speaking of course. Unfortunately that renders much of science to be logically absurd, and its practitioners to be trusted at the approximate level of politicians, child molesters and Atheists. (I'm sure there is a Venn diagram of that somewhere...).

Another Scientific Principle is Dropped

Everyone recommends flossing – but there's hardly any proof it works

Dental organizations and the federal government have long urged people to use dental floss, but the latest US guidelines have dropped the recommendation


"It’s one of the most universal recommendations in all of public health: floss daily to prevent gum disease and cavities.

Except there’s little proof that flossing works.

Still, governments, dental organisations and manufacturers of floss have pushed the practice for decades. Dentists provide samples to their patients; the British Dental Association insists on its patient website that flossing helps “in the battle against tooth decay and gum disease”.

But all this could change following an investigation by Associated Press (AP). Last year journalists from the agency asked the departments of health and human services and agriculture in the US for their evidence that flossing works.

Since then, the US government has quietly dropped the recommendation, admitting that there is no scientific evidence to prove the benefits. And now the NHS is set to review their own guidelines.

On its website, it currently states that dental floss “helps to prevent gum disease by getting rid of pieces of food and plaque from between your teeth” which can cause inflammation.

A leading British dentist, however, said there is only “weak evidence” that flossing helps in this way. Professor Damien Walmsley of Birmingham University, said the time and expense required for reliable studies meant the health claims often attributed to floss were unproven. "

40,000 fMRI Studies: Trashed

Due to a bad assumption used in the statistical analysis of fMRI data, it has been determined that there is a 70% false positive rate for the automatic determination of the value of a "voxel" (smallest unit of granularity). This apparently is because of the assumption of a Gaussian distribution for all the clusters of data, which is not the case in real life. This fully invalidates a huge swath of fMRI studies:

The Future of fMRI.

It is not feasible to redo 40,000 fMRI studies, and lamentable archiving and data-sharing practices mean most could not be reanalyzed either. Considering that it is now possible to evaluate common statistical methods using real fMRI data, the fMRI community should, in our opinion, focus on validation of existing methods. The main drawback of a permutation test is the increase in computational complexity, as the group analysis needs to be repeated 1,000–10,000 times. However, this increased processing time is not a problem in practice, as for typical sample sizes a desktop computer can run a permutation test for neuroimaging data in less than a minute (27, 43). Although we note that metaanalysis can play an important role in teasing apart false-positive findings from consistent results, that does not mitigate the need for accurate inferential tools that give valid results for each and every study.

Finally, we point out the key role that data sharing played in this work and its impact in the future. Although our massive empirical study depended on shared data, it is disappointing that almost none of the published studies have shared their data, neither the original data nor even the 3D statistical maps. As no analysis method is perfect, and new problems and limitations will be certainly found in the future, we commend all authors to at least share their statistical results [e.g., via NeuroVault.org (44)] and ideally the full data [e.g., via OpenfMRI.org (7)]. Such shared data provide enormous opportunities for methodologists, but also the ability to revisit results when methods improve years later.

More "Pseudoscience" Involving Activists

Glyphosate, the MMR vaccine and pseudoscience

A large dossier claiming to find evidence that glyphosate is “probably carcinogenic” was published last year by the International Agency for Research on Cancer (IARC), part of the World Health Organisation. What could be more scientifically respectable?

Yet the document depends heavily on the work of an activist employed by a pressure group called the Environmental Defense Fund: Christopher Portier, whose conflict of interest the IARC twice omitted to disclose. Portier chaired the committee that proposed a study on glyphosate and then served as technical adviser to the IARC’s glyphosate report team, even though he is not a toxicologist. He has since been campaigning against glyphosate.

The IARC study is surely pseudoscience. It relies on a tiny number of cherry-picked studies, and even these don’t support its conclusion. The evidence that it causes cancer in humans is especially tenuous, based on three epidemiological studies with confounding factors and small sample sizes “linking” it to Non-Hodgkin lymphoma (NHL). The study ignored the US Agricultural Health Study, which has been tracking some 89,000 farmers and their spouses for 23 years.

The study found “no association between glyphosate exposure and all cancer incidence or most of the specific cancer subtypes we evaluated, including NHL . . .”

Many other studies found very little cancer risk from glyphosate use, but the IARC argued that they included some data generated by industry. Well, of course they did, because we rightly demand that industry, not the taxpayer, pays for and does the safety testing of its products and makes the results public. The IARC appeared to ignore work by the German Federal Institute for Risk Assessment, managing the glyphosate dossier for the European Commission, which judged glyphosate safe. As did the European Food Safety Authority, whose head accused the IARC and Portier of bringing in the “Facebook age of science”.

[...]

James Gurney, a microbiologist who blogs on a site called the League of Nerds, describes the level of scholarship in the IARC report as “on a par with Andrew Wakefield of MMR/autism fame”.

In the case of Mr Wakefield’s claim that the measles, mumps and rubella (MMR) vaccine causes autism, the push-back against pseudoscience largely succeeded in this country, though not before real harm had been done. Journalists found that Mr Wakefield had failed to declare financing from lawyers preparing to sue vaccine makers and had taken blood samples at his own children’s party; further research failed to replicate his results. His paper was retracted and he was struck off the medical register, the General Medical Council calling him dishonest and irresponsible. His message is now falling on fertile ground in the United States, however, where measles epidemics have resumed as a result.

In both these cases, superficial plausibility is lent to the scares by history. Earlier pesticides were more dangerous: copper sulphate (still used as a fungicide by “organic” farmers) is toxic; DDT insecticide did wipe out predatory birds; paraquat herbicide was used in suicides. But Roundup is far, far less dangerous than these.

Likewise, early vaccines did carry risks. In the 1950s polio vaccines, grown in monkey tissue, were contaminated with SV40, a virus associated with cancer in monkeys. Many children were infected with the virus as a result. Fortunately, SV40 proved neither infectious nor carcinogenic in human beings, but it was a bullet dodged. Today such contamination is impossible.

Pseudoscience is bad enough when it infects astrologers, 9/11 truthers and crop-circle makers. But when its symptoms show up in mainstream bodies, such as the World Health Organisation, it’s time to be worried.

Science is vulnerable to infection from activists, greedy manipulators, and gullible Scientism. Giving too much credence to the contingent factoids produced by scientists who are under pressure to support politically correct data makes science into a dark art, when it should be a pristine intellectual pursuit.

Science and Reality

Scientific Regress
The problem with ­science is that so much of it simply isn’t.
by William A. Wilson
May 2016

Last summer, the Open Science Collaboration announced that it had tried to replicate one hundred published psychology experiments sampled from three of the most prestigious journals in the field. Scientific claims rest on the idea that experiments repeated under nearly identical conditions ought to yield approximately the same results, but until very recently, very few had bothered to check in a systematic way whether this was actually the case. The OSC was the biggest attempt yet to check a field’s results, and the most shocking. In many cases, they had used original experimental materials, and sometimes even performed the experiments under the guidance of the original researchers. Of the studies that had originally reported positive results, an astonishing 65 percent failed to show statistical significance on replication, and many of the remainder showed greatly reduced effect sizes.

Their findings made the news, and quickly became a club with which to bash the social sciences. But the problem isn’t just with psychology. There’s an ­unspoken rule in the pharmaceutical industry that half of all academic biomedical research will ultimately prove false, and in 2011 a group of researchers at Bayer decided to test it. Looking at sixty-seven recent drug discovery projects based on preclinical cancer biology research, they found that in more than 75 percent of cases the published data did not match up with their in-house attempts to replicate. These were not studies published in fly-by-night oncology journals, but blockbuster research featured in Science, Nature, Cell, and the like. The Bayer researchers were drowning in bad studies, and it was to this, in part, that they attributed the mysteriously declining yields of drug pipelines. Perhaps so many of these new drugs fail to have an effect because the basic research on which their development was based isn’t valid.

When a study fails to replicate, there are two possible interpretations. The first is that, unbeknownst to the investigators, there was a real difference in experimental setup between the original investigation and the failed replication. These are colloquially referred to as “wallpaper effects,” the joke being that the experiment was affected by the color of the wallpaper in the room. This is the happiest possible explanation for failure to reproduce: It means that both experiments have revealed facts about the universe, and we now have the opportunity to learn what the difference was between them and to incorporate a new and subtler distinction into our theories.

The other interpretation is that the original finding was false. Unfortunately, an ingenious statistical argument shows that this second interpretation is far more likely. First articulated by John Ioannidis, a professor at Stanford University’s School of Medicine, this argument proceeds by a simple application of Bayesian statistics. Suppose that there are a hundred and one stones in a certain field. One of them has a diamond inside it, and, luckily, you have a diamond-detecting device that advertises 99 percent accuracy. After an hour or so of moving the device around, examining each stone in turn, suddenly alarms flash and sirens wail while the device is pointed at a promising-looking stone. What is the probability that the stone contains a diamond?

Most would say that if the device advertises 99 percent accuracy, then there is a 99 percent chance that the device is correctly discerning a diamond, and a 1 percent chance that it has given a false positive reading. But consider: Of the one hundred and one stones in the field, only one is truly a diamond. Granted, our machine has a very high probability of correctly declaring it to be a diamond. But there are many more diamond-free stones, and while the machine only has a 1 percent chance of falsely declaring each of them to be a diamond, there are a hundred of them. So if we were to wave the detector over every stone in the field, it would, on average, sound twice—once for the real diamond, and once when a false reading was triggered by a stone. If we know only that the alarm has sounded, these two possibilities are roughly equally probable, giving us an approximately 50 percent chance that the stone really contains a diamond.

This is a simplified version of the argument that Ioannidis applies to the process of science itself. The stones in the field are the set of all possible testable hypotheses, the diamond is a hypothesized connection or effect that happens to be true, and the diamond-detecting device is the scientific method. A tremendous amount depends on the proportion of possible hypotheses which turn out to be true, and on the accuracy with which an experiment can discern truth from falsehood. Ioannidis shows that for a wide variety of scientific settings and fields, the values of these two parameters are not at all favorable.

For instance, consider a team of molecular biologists investigating whether a mutation in one of the countless thousands of human genes is linked to an increased risk of Alzheimer’s. The probability of a randomly selected mutation in a randomly selected gene having precisely that effect is quite low, so just as with the stones in the field, a positive finding is more likely than not to be spurious—unless the experiment is unbelievably successful at sorting the wheat from the chaff. Indeed, Ioannidis finds that in many cases, approaching even 50 percent true positives requires unimaginable accuracy. Hence the eye-catching title of his paper: “Why Most Published Research Findings Are False.”

What about accuracy? Here, too, the news is not good. First, it is a de facto standard in many fields to use one in twenty as an acceptable cutoff for the rate of false positives. To the naive ear, that may sound promising: Surely it means that just 5 percent of scientific studies report a false positive? But this is precisely the same mistake as thinking that a stone has a 99 percent chance of containing a ­diamond just because the detector has sounded. What it really means is that for each of the countless false hypo­theses that are contemplated by researchers, we accept a 5 percent chance that it will be falsely counted as true—a decision with a considerably more deleterious effect on the proportion of correct studies.

Paradoxically, the situation is actually made worse by the fact that a promising connection is often studied by several independent teams. To see why, suppose that three groups of researchers are studying a phenomenon, and when all the data are analyzed, one group announces that it has discovered a connection, but the other two find nothing of note. Assuming that all the tests involved have a high statistical power, the lone positive finding is almost certainly the spurious one. However, when it comes time to report these findings, what happens? The teams that found a negative result may not even bother to write up their non-discovery. After all, a report that a fanciful connection probably isn’t true is not the stuff of which scientific prizes, grant money, and tenure decisions are made.

And even if they did write it up, it probably wouldn’t be accepted for publication. Journals are in competition with one another for attention and “impact factor,” and are always more eager to report a new, exciting finding than a killjoy failure to find an association. In fact, both of these effects can be quantified. Since the majority of all investigated hypotheses are false, if positive and negative evidence were written up and accepted for publication in equal proportions, then the majority of articles in scientific journals should report no findings. When tallies are actually made, though, the precise opposite turns out to be true: Nearly every published scientific article reports the presence of an association. There must be massive bias at work.

Ioannidis’s argument would be potent even if all scientists were angels motivated by the best of intentions, but when the human element is considered, the picture becomes truly dismal. Scientists have long been aware of something euphemistically called the “experimenter effect”: the curious fact that when a phenomenon is investigated by a researcher who happens to believe in the phenomenon, it is far more likely to be detected. Much of the effect can likely be explained by researchers unconsciously giving hints or suggestions to their human or animal subjects, perhaps in something as subtle as body language or tone of voice. Even those with the best of intentions have been caught fudging measurements, or making small errors in rounding or in statistical analysis that happen to give a more favorable result. Very often, this is just the result of an honest statistical error that leads to a desirable outcome, and therefore it isn’t checked as deliberately as it might have been had it pointed in the opposite direction.

But, and there is no putting it nicely, deliberate fraud is far more widespread than the scientific establishment is generally willing to admit. One way we know that there’s a great deal of fraud occurring is that if you phrase your question the right way, ­scientists will confess to it. In a survey of two thousand research psychologists conducted in 2011, over half of those surveyed admitted outright to selectively reporting those experiments which gave the result they were after. Then the investigators asked respondents anonymously to estimate how many of their fellow scientists had engaged in fraudulent behavior, and promised them that the more accurate their guesses, the larger a contribution would be made to the charity of their choice. Through several rounds of anonymous guessing, refined using the number of scientists who would admit their own fraud and other indirect measurements, the investigators concluded that around 10 percent of research psychologists have engaged in outright falsification of data, and more than half have engaged in less brazen but still fraudulent behavior such as reporting that a result was statistically significant when it was not, or deciding between two different data analysis techniques after looking at the results of each and choosing the more favorable.

Many forms of statistical falsification are devilishly difficult to catch, or close enough to a genuine judgment call to provide plausible deniability. Data analysis is very much an art, and one that affords even its most scrupulous practitioners a wide degree of latitude. Which of these two statistical tests, both applicable to this situation, should be used? Should a subpopulation of the research sample with some common criterion be picked out and reanalyzed as if it were the totality? Which of the hundreds of coincident factors measured should be controlled for, and how? The same freedom that empowers a statistician to pick a true signal out of the noise also enables a dishonest scientist to manufacture nearly any result he or she wishes. Cajoling statistical significance where in reality there is none, a practice commonly known as “p-hacking,” is particularly easy to accomplish and difficult to detect on a case-by-case basis. And since the vast majority of studies still do not report their raw data along with their findings, there is often nothing to re-analyze and check even if there were volunteers with the time and inclination to do so.

One creative attempt to estimate how widespread such dishonesty really is involves comparisons between fields of varying “hardness.” The author, Daniele Fanelli, theorized that the farther from physics one gets, the more freedom creeps into one’s experimental methodology, and the fewer constraints there are on a scientist’s conscious and unconscious biases. If all scientists were constantly attempting to influence the results of their analyses, but had more opportunities to do so the “softer” the science, then we might expect that the social sciences have more papers that confirm a sought-after hypothesis than do the physical sciences, with medicine and biology somewhere in the middle. This is exactly what the study discovered: A paper in psychology or psychiatry is about five times as likely to report a positive result as one in astrophysics. This is not necessarily evidence that psychologists are all consciously or unconsciously manipulating their data—it could also be evidence of massive publication bias—but either way, the result is disturbing.

Speaking of physics, how do things go with this hardest of all hard sciences? Better than elsewhere, it would appear, and it’s unsurprising that those who claim all is well in the world of science reach so reliably and so insistently for examples from physics, preferably of the most theoretical sort. Folk histories of physics combine borrowed mathematical luster and Whiggish triumphalism in a way that journalists seem powerless to resist. The outcomes of physics experiments and astronomical observations seem so matter-of-fact, so concretely and immediately connected to underlying reality, that they might let us gingerly sidestep all of these issues concerning motivated or sloppy analysis and interpretation. “E pur si muove,” Galileo is said to have remarked, and one can almost hear in his sigh the hopes of a hundred science journalists for whom it would be all too convenient if Nature were always willing to tell us whose theory is more correct.

And yet the flight to physics rather gives the game away, since measured any way you like—volume of papers, number of working researchers, total amount of funding—deductive, theory-building physics in the mold of Newton and Lagrange, Maxwell and Einstein, is a tiny fraction of modern science as a whole. In fact, it also makes up a tiny fraction of modern physics. Far more common is the delicate and subtle art of scouring inconceivably vast volumes of noise with advanced software and mathematical tools in search of the faintest signal of some hypothesized but never before observed phenomenon, whether an astrophysical event or the decay of a subatomic particle. This sort of work is difficult and beautiful in its own way, but it is not at all self-evident in the manner of a falling apple or an elliptical planetary orbit, and it is very sensitive to the same sorts of accidental contamination, deliberate fraud, and unconscious bias as the medical and social-scientific studies we have discussed. Two of the most vaunted physics results of the past few years—the announced discovery of both cosmic inflation and gravitational waves at the BICEP2 experiment in Antarctica, and the supposed discovery of superluminal neutrinos at the Swiss-Italian border—have now been retracted, with far less fanfare than when they were first published.

Many defenders of the scientific establishment will admit to this problem, then offer hymns to the self-correcting nature of the scientific method. Yes, the path is rocky, they say, but peer review, competition between researchers, and the comforting fact that there is an objective reality out there whose test every theory must withstand or fail, all conspire to mean that sloppiness, bad luck, and even fraud are exposed and swept away by the advances of the field.

So the dogma goes. But these claims are rarely treated like hypotheses to be tested. Partisans of the new scientism are fond of recounting the “Sokal hoax”—physicist Alan Sokal submitted a paper heavy on jargon but full of false and meaningless statements to the postmodern cultural studies journal Social Text, which accepted and published it without quibble—but are unlikely to mention a similar experiment conducted on reviewers of the prestigious British Medical Journal. The experimenters deliberately modified a paper to include eight different major errors in study design, methodology, data analysis, and interpretation of results, and not a single one of the 221 reviewers who participated caught all of the errors. On average, they caught fewer than two—and, unbelievably, these results held up even in the subset of reviewers who had been specifically warned that they were participating in a study and that there might be something a little odd in the paper that they were reviewing. In all, only 30 percent of reviewers recommended that the intentionally flawed paper be rejected.

If peer review is good at anything, it appears to be keeping unpopular ideas from being published. Consider the finding of another (yes, another) of these replicability studies, this time from a group of cancer researchers. In addition to reaching the now unsurprising conclusion that only a dismal 11 percent of the preclinical cancer research they examined could be validated after the fact, the authors identified another horrifying pattern: The “bad” papers that failed to replicate were, on average, cited far more often than the papers that did! As the authors put it, “some non-reproducible preclinical papers had spawned an entire field, with hundreds of secondary publications that expanded on elements of the original observation, but did not actually seek to confirm or falsify its fundamental basis.”

What they do not mention is that once an entire field has been created—with careers, funding, appointments, and prestige all premised upon an experimental result which was utterly false due either to fraud or to plain bad luck—pointing this fact out is not likely to be very popular. Peer review switches from merely useless to actively harmful. It may be ineffective at keeping papers with analytic or methodological flaws from being published, but it can be deadly effective at suppressing criticism of a dominant research paradigm. Even if a critic is able to get his work published, pointing out that the house you’ve built together is situated over a chasm will not endear him to his colleagues or, more importantly, to his mentors and patrons.

Older scientists contribute to the propagation of scientific fields in ways that go beyond educating and mentoring a new generation. In many fields, it’s common for an established and respected researcher to serve as “senior author” on a bright young star’s first few publications, lending his prestige and credibility to the result, and signaling to reviewers that he stands behind it. In the natural sciences and medicine, senior scientists are frequently the controllers of laboratory resources—which these days include not just scientific instruments, but dedicated staffs of grant proposal writers and regulatory compliance experts—without which a young scientist has no hope of accomplishing significant research. Older scientists control access to scientific prestige by serving on the editorial boards of major journals and on university tenure-review committees. Finally, the government bodies that award the vast majority of scientific funding are either staffed or advised by distinguished practitioners in the field.

All of which makes it rather more bothersome that older scientists are the most likely to be invested in the regnant research paradigm, whatever it is, even if it’s based on an old experiment that has never successfully been replicated. The quantum physicist Max Planck famously quipped: “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” Planck may have been too optimistic. A recent paper from the National Bureau of Economic Research studied what happens to scientific subfields when star researchers die suddenly and at the peak of their abilities, and finds that while there is considerable evidence that young researchers are reluctant to challenge scientific superstars, a sudden and unexpected death does not significantly improve the situation, particularly when “key collaborators of the star are in a position to channel resources (such as editorial goodwill or funding) to insiders.”

In the idealized Popperian view of scientific progress, new theories are proposed to explain new evidence that contradicts the predictions of old theories. The heretical philosopher of science Paul Feyerabend, on the other hand, claimed that new theories frequently contradict the best available evidence—at least at first. Often, the old observations were inaccurate or irrelevant, and it was the invention of a new theory that stimulated experimentalists to go hunting for new observational techniques to test it. But the success of this “unofficial” process depends on a blithe disregard for evidence while the vulnerable young theory weathers an initial storm of skepticism. Yet if Feyerabend is correct, and an unpopular new theory can ignore or reject experimental data long enough to get its footing, how much longer can an old and creaky theory, buttressed by the reputations and influence and political power of hundreds of established practitioners, continue to hang in the air even when the results upon which it is premised are exposed as false?

The hagiographies of science are full of paeans to the self-correcting, self-healing nature of the enterprise. But if raw results are so often false, the filtering mechanisms so ineffective, and the self-correcting mechanisms so compromised and slow, then science’s approach to truth may not even be monotonic. That is, past theories, now “refuted” by evidence and replaced with new approaches, may be closer to the truth than what we think now. Such regress has happened before: In the nineteenth century, the (correct) vitamin C deficiency theory of scurvy was replaced by the false belief that scurvy was caused by proximity to spoiled foods. Many ancient astronomers believed the heliocentric model of the solar system before it was supplanted by the geocentric theory of Ptolemy. The Whiggish view of scientific history is so dominant today that this possibility is spoken of only in hushed whispers, but ours is a world in which things once known can be lost and buried.

And even if self-correction does occur and theories move strictly along a lifecycle from less to more accurate, what if the unremitting flood of new, mostly false, results pours in faster? Too fast for the sclerotic, compromised truth-discerning mechanisms of science to operate? The result could be a growing body of true theories completely overwhelmed by an ever-larger thicket of baseless theories, such that the proportion of true scientific beliefs shrinks even while the absolute number of them continues to rise. Borges’s Library of Babel contained every true book that could ever be written, but it was useless because it also contained every false book, and both true and false were lost within an ocean of nonsense.

Which brings us to the odd moment in which we live. At the same time as an ever more bloated scientific bureaucracy churns out masses of research results, the majority of which are likely outright false, scientists themselves are lauded as heroes and science is upheld as the only legitimate basis for policy-making. There’s reason to believe that these phenomena are linked. When a formerly ascetic discipline suddenly attains a measure of influence, it is bound to be flooded by opportunists and charlatans, whether it’s the National Academy of Science or the monastery of Cluny.

This comparison is not as outrageous as it seems: Like monasticism, science is an enterprise with a superhuman aim whose achievement is forever beyond the capacities of the flawed humans who aspire toward it. The best scientists know that they must practice a sort of mortification of the ego and cultivate a dispassion that allows them to report their findings, even when those findings might mean the dashing of hopes, the drying up of financial resources, and the loss of professional prestige. It should be no surprise that even after outgrowing the monasteries, the practice of science has attracted souls driven to seek the truth regardless of personal cost and despite, for most of its history, a distinct lack of financial or status reward. Now, however, science and especially science bureaucracy is a career, and one amenable to social climbing. Careers attract careerists, in Feyerabend’s words: “devoid of ideas, full of fear, intent on producing some paltry result so that they can add to the flood of inane papers that now constitutes ‘scientific progress’ in many areas.”

If science was unprepared for the influx of careerists, it was even less prepared for the blossoming of the Cult of Science. The Cult is related to the phenomenon described as “scientism”; both have a tendency to treat the body of scientific knowledge as a holy book or an a-religious revelation that offers simple and decisive resolutions to deep questions. But it adds to this a pinch of glib frivolity and a dash of unembarrassed ignorance. Its rhetorical tics include a forced enthusiasm (a search on Twitter for the hashtag “#sciencedancing” speaks volumes) and a penchant for profanity. Here in Silicon Valley, one can scarcely go a day without seeing a t-shirt reading “Science: It works, b—es!” The hero of the recent popular movie The Martian boasts that he will “science the sh— out of” a situation. One of the largest groups on Facebook is titled “I f—ing love Science!” (a name which, combined with the group’s penchant for posting scarcely any actual scientific material but a lot of pictures of natural phenomena, has prompted more than one actual scientist of my acquaintance to mutter under her breath, “What you truly love is pictures”). Some of the Cult’s leaders like to play dress-up as scientists—Bill Nye and Neil deGrasse Tyson are two particularly prominent examples— but hardly any of them have contributed any research results of note. Rather, Cult leadership trends heavily in the direction of educators, popularizers, and journalists.

At its best, science is a human enterprise with a superhuman aim: the discovery of regularities in the order of nature, and the discerning of the consequences of those regularities. We’ve seen example after example of how the human element of this enterprise harms and damages its progress, through incompetence, fraud, selfishness, prejudice, or the simple combination of an honest oversight or slip with plain bad luck. These failings need not hobble the scientific enterprise broadly conceived, but only if scientists are hyper-aware of and endlessly vigilant about the errors of their colleagues . . . and of themselves. When cultural trends attempt to render science a sort of religion-less clericalism, scientists are apt to forget that they are made of the same crooked timber as the rest of humanity and will necessarily imperil the work that they do. The greatest friends of the Cult of Science are the worst enemies of science’s actual practice.
[All emphasis added]

Smarter? Or Stupider? More Unsettled Science

The Flynn Effect: A Meta-analysis

The Flynn Effect has long been accepted. It says that general population IQ has increased at 0.3 points per annum, meaning 30 points increase over the past 100 years.

Now there is contrary evidence:

The decline in general intelligence estimated from a meta-analysis

This study claims that general IQ has decreased at the rate of -1.16 points per decade, or -11.6 points in the last century.

My guess is that both conclusions are trivial and without intellectual value. The very definition of intelligence changes over time, with various subcategories springing up, such as verbal, mathematical, mechanical aptitude, etc. And then these categories morph into something else. The second study, above, merely depends upon reaction time, which would put boxers and race drivers over engineers and professors, I would imagine.

Either way, the science - if you choose to allow it to be called such - is unsettled. And these conclusions are pretty much meaningless. Your observation of general intelligence is very likely as meaningful as either of these studies and their conclusions.

Another Settled Science Bites the Dust

Full-fat milk 'may drastically reduce risk of diabetes' - study

"The 15-year study, in which researchers analysed the blood of 3,333 adults aged between 30 and 75, found that people with higher levels of dairy fat in their systems had as much as a 46 percent lower risk of diabetes than those who regularly consumed only low-fat foods.

The research team at Tufts Friedman School of Nutrition Science & Policy looked at data from the Nurses’ Health Study of Health Professionals.

“There is no prospective human evidence that people who eat low-fat dairy do better than people who eat whole-fat dairy,” said researcher Dr. Dariush Mozaffarian.

However, he cautioned that the research results were preliminary and shouldn't be taken as official guidance on diet: “The implications aren’t yet to tell people definitely to drink only whole milk and avoid skim milk,” he said.

However, speaking to Time he said: “In the absence of any evidence for the superior effects of low fat dairy, and some evidence that there may be better benefits of whole fat dairy products for diabetes, why are we recommending only low fat diary? We should be telling people to eat a variety of dairy and remove the recommendation about fat content.”

Dr. Susan Spratt, a diabetes specialist and assistant professor of medicine at Duke University School of Medicine, told CBS: "I think we now understand there are healthy fats and unhealthy fats; healthy carbohydrates and less healthy carbohydrates. And fat can improve satiety and that could reduce total calorie intake."

Dietary science is noticeably non-replicable. Plus, the guidelines are heavily lobbied by food industries. It's a wonder that sugar is not at the top of the official food pyramid, since it is in nearly everything.

The sugar conspiracy
In 1972, a British scientist sounded the alarm that sugar – and not fat – was the greatest danger to our health. But his findings were ridiculed and his reputation ruined. How did the world’s top nutrition scientists get it so wrong for so long?

"In 1980, after long consultation with some of America’s most senior nutrition scientists, the US government issued its first Dietary Guidelines. The guidelines shaped the diets of hundreds of millions of people. Doctors base their advice on them, food companies develop products to comply with them. Their influence extends beyond the US. In 1983, the UK government issued advice that closely followed the American example.

The most prominent recommendation of both governments was to cut back on saturated fats and cholesterol (this was the first time that the public had been advised to eat less of something, rather than enough of everything). Consumers dutifully obeyed. We replaced steak and sausages with pasta and rice, butter with margarine and vegetable oils, eggs with muesli, and milk with low-fat milk or orange juice. But instead of becoming healthier, we grew fatter and sicker.

Look at a graph of postwar obesity rates and it becomes clear that something changed after 1980. In the US, the line rises very gradually until, in the early 1980s, it takes off like an aeroplane. Just 12% of Americans were obese in 1950, 15% in 1980, 35% by 2000. In the UK, the line is flat for decades until the mid-1980s, at which point it also turns towards the sky. Only 6% of Britons were obese in 1980. In the next 20 years that figure more than trebled. Today, two thirds of Britons are either obese or overweight, making this the fattest country in the EU. Type 2 diabetes, closely related to obesity, has risen in tandem in both countries.

At best, we can conclude that the official guidelines did not achieve their objective; at worst, they led to a decades-long health catastrophe. Naturally, then, a search for culprits has ensued. Scientists are conventionally apolitical figures, but these days, nutrition researchers write editorials and books that resemble liberal activist tracts, fizzing with righteous denunciations of “big sugar” and fast food. Nobody could have predicted, it is said, how the food manufacturers would respond to the injunction against fat – selling us low-fat yoghurts bulked up with sugar, and cakes infused with liver-corroding transfats.

Nutrition scientists are angry with the press for distorting their findings, politicians for failing to heed them, and the rest of us for overeating and under-exercising. In short, everyone – business, media, politicians, consumers – is to blame. Everyone, that is, except scientists.

But it was not impossible to foresee that the vilification of fat might be an error. Energy from food comes to us in three forms: fat, carbohydrate, and protein. Since the proportion of energy we get from protein tends to stay stable, whatever our diet, a low-fat diet effectively means a high-carbohydrate diet. The most versatile and palatable carbohydrate is sugar, which John Yudkin had already circled in red. In 1974, the UK medical journal, the Lancet, sounded a warning about the possible consequences of recommending reductions in dietary fat: “The cure should not be worse than the disease.”

ADDENDUM:
The Science of Bad Science

"In a 2015 paper titled Does Science Advance One Funeral at a Time?, a team of scholars at the National Bureau of Economic Research sought an empirical basis for a remark made by the physicist Max Planck: “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”

The researchers identified more than 12,000 “elite” scientists from different fields. The criteria for elite status included funding, number of publications, and whether they were members of the National Academies of Science or the Institute of Medicine. Searching obituaries, the team found 452 who had died before retirement. They then looked to see what happened to the fields from which these celebrated scientists had unexpectedly departed, by analysing publishing patterns.

What they found confirmed the truth of Planck’s maxim. Junior researchers who had worked closely with the elite scientists, authoring papers with them, published less. At the same time, there was a marked increase in papers by newcomers to the field, who were less likely to cite the work of the deceased eminence. The articles by these newcomers were substantive and influential, attracting a high number of citations. They moved the whole field along.

A scientist is part of what the Polish philosopher of science Ludwik Fleck called a “thought collective”: a group of people exchanging ideas in a mutually comprehensible idiom. The group, suggested Fleck, inevitably develops a mind of its own, as the individuals in it converge on a way of communicating, thinking and feeling.

This makes scientific inquiry prone to the eternal rules of human social life: deference to the charismatic, herding towards majority opinion, punishment for deviance, and intense discomfort with admitting to error. Of course, such tendencies are precisely what the scientific method was invented to correct for, and over the long run, it does a good job of it. In the long run, however, we’re all dead, quite possibly sooner than we would be if we hadn’t been following a diet based on poor advice."

Bad Science on Breast Feeding

The breastfeeding story is more complicated than you think

"To conduct their research, the authors of the studies, published in the medical journal The Lancet, combed through the massive amount of medical literature that has amassed on the best way to feed infants -- and found it is rife with low-quality studies and potentially biased results. They had to root out many studies in order to get to their estimate of breastfeeding's true benefit.

The popular notion of breast milk as a panacea has stemmed from dozens of studies that have reported benefits ranging from a lower risk of asthma to increased IQ. But flaws in some of the studies tracking long-term health effects raise questions about the magnitude -- and at times the existence -- of some of those advantages. Many fail to properly control for factors such as the mothers' education and wealth. When those effects are accounted for, the benefits almost always shrink.

[...]

In other words, some of the advantage that studies ascribe to mothers' milk can be explained by infants' environments and parents, particularly in high income countries. For some researchers, that raises questions about whether efforts aimed at increasing breastfeeding rates in high-income countries will have the expected results.

"Sure, the children might be slightly better off because of being breastfed," said Cynthia Colen, a sociologist at Ohio State University. "But we haven’t changed anything about the environment in which they live: lead in the water, under-performing schools. I don’t think you’re really solving the problem; it's almost passing the buck.""

More Darwinian-type correlation-as-causation type bogus science.

Study: Transparency Lacking in Biomedical Literature

Study: Transparency Lacking in Biomedical Literature

Few authors make their full data available and most published papers do not clearly state funding sources and conflicts of interest.

Despite a push for transparency in science, full data disclosure may be close to non-existent among published studies. Of 441 randomly selected biomedical research papers analyzed in a new study, none provided access to all the authors’ data. And only one of these papers shared a complete protocol. The results of this analysis, which could shed light on science’s reproducibility problem, were published today (January 4 [2016]) in PLOS Biology.

“What was most surprising to me was the complete lack of data-sharing and protocol availability,” said study coauthor John Ioannidis, a professor of medicine and health research and policy at the Stanford University School of Medicine. “That was worse than I would have predicted.”

This is a complete failure and abandonment of empirical scientific process. Apparently Journals have to have "stuff" to publish; and peer review is either superficial, non-existent, or done by collusion. There is no possible capture of science fraud in this environment, unless someone puts resources into replication, without full knowledge of the actual experimental process. And that likely happens only for subjects which provoke both interest and suspicion.

This is indicative of an internally corrupt culture, one that has values other than empiricism.

More Bogus Science Debunked: Scientismists Hit Hardest

Scientists say happiness won't extend your life after all

Happy people live longer, a relationship that’s been documented in a variety of research studies.

But a new paper published in the medical journal Lancet comes to the sad conclusion that happiness isn’t responsible for this observed longevity. Instead, the things that make people happy, particularly their good health, are the same things that shield them from premature death.

“Happiness and related measures of well-being do not appear to have any direct effect on mortality,” the study authors wrote.

Correlation is not causation. Too much "science" is merely correlation, not actual causal fact.

More Phony Science

Got Incompetence? The Federal Gov't Has Misled Public About Milk For Decades

"If you look up "whole milk" in the government's official Dietary Guidelines, it states pretty definitively that people should only drink skim or 1% milk. "If you currently drink whole milk," it says, "gradually switch to lower fat versions."

This is the same advice the government has been issuing for many years. And it's wrong.

Research published in recent years shows that people "might have been better off had they stuck with whole milk," according to a front-page story in the Washington Post on Wednesday. "People who consumed more milk fat had lower incidence of heart disease."

The story goes on to note that the government's push for Americans to eat a high-carb diet "provokes a number of heart disease risk factors."

As the Harvard School of Public Health's Walter Willett put it, the "campaign to reduce fat in the diet has had some pretty disastrous consequences."

The Post goes on to note that this "has raised questions about the scientific foundations of the government's diet advice."

It should.

Based on flimsy evidence, the USDA first started urging people to eat low-fat diets in 1977. As evidence grew that this advice was misguided — at best — it steadfastly refused to change course.

So what we have here is the U.S. government using its power and might to push Americans — quite successfully — to change their eating habits in ways that likely killed many of them.

If a private enterprise had done this, it would face massive class action lawsuits, its executives would be in jail, and its reputation permanently ruined.

But the government simply brushes off its own disasters, and goes right on telling people what they should and shouldn't eat. The public would do well to tell government officials to stay out of the kitchen."

The US government couldn't develop more distrust of science if it actually tried.

More Bad Science "Fact" Bites The Dirt

If you can call economics a science. But it is used for political policy.

From the Kaus Files:

Mariel, Farewell?

"The law of supply and demand suggests that an increase in immigrant labor will lower wages. That’s what opponents of increased immigration (including opponents of Rubio-style “comprehensive” reform) say. For decades, the major bit of discordant data has been Prof. David Card’s study of the Mariel boatlift — a “natural experiment” in which 125,000 Cubans landed in Miami in 1980. Did they suppress local wages? Card found that they didn’t — a counterintuitive finding often cited in surveys of the academic debate on immigration, such as this New York Times Magazine article

Card documented that blacks, and also other workers, in Miami actually did better than in the control cities. …

Not so fast! Prof. George Borjas, whose work tends to uphold the conventional supply-demand view, has just gone back and reexamined the Mariel data, looking specificially at what happened to less-skilled workers (high school dropouts). It turns out they took a beating!

The absolute wage of high school dropouts in Miami dropped dramatically, as did the wage of high school dropouts relative to that of either high school graduates or college graduates. The drop in the relative wage of the least educated Miamians was substantial (10 to 30 percent). … [E.A.]

Card may have missed this because he lumped high school dropouts in with high school graduates, whose wage was unaffected. (At least 60% of the Marielitos were in the lowest skilled, high-school dropout group.)

Borjas also compares Miami’s experience with cities that had similar growth patterns before the Mariel labor “supply shock” (as opposed to Card’s “control” cities, which were chosen partly because they had similar growth patterns after the shock). Borjas finds “the relative decline in the wage of low-educated workers in Miami is much larger when we compare Miami to cities that had comparable employment growth.” It makes sense that if wages in Miami went down, but you compare Miami only with cities where wages also went down, you’ might miss some of the relative Miami decline.

In both academic and political terms, this is a BFD. It looks like the law of supply and demand works. More immigrant workers translates into lower wages. The most conspicuous, unassailable finding to the contrary has apparently just been demolished. A major prop in the arguments for greater low-skilled immigration (including arguments for amnesty) –‘What about Mariel?’ — would seem to have disappeared, though the other side has yet to be heard from. (And they will be heard from.) Borjas’ study only just went public."

Card compared apples to oranges and got wrong answers. That is the problem in using science - unreplicated and untested for falsification - as political policy. The law of Supply and Demand goes directly and diametrically opposite to Marxist, top down control of everything. The Left will wail and flail at this falsification of "the failure of Supply and Demand economics" in massive immigration influx conditions.

Actual fact: it did and it will affect black Americans and poor people.

Does the Left really care about blacks and the poor? We'll see.

More Bogus Science

Study delivers bleak verdict on validity of psychology experiment results

"Bleak" is too generous.

"In the investigation, a whopping 75% of the social psychology experiments were not replicated, meaning that the originally reported findings vanished when other scientists repeated the experiments. Half of the cognitive psychology studies failed the same test. Details are published in the journal Science.

Even when scientists could replicate original findings, the sizes of the effects they found were on average half as big as reported first time around. That could be due to scientists leaving out data that undermined their hypotheses, and by journals accepting only the strongest claims for publication."

Every finding should be replicated before publication. And falsifications should be published as well: a falsified experiment is as valuable a piece of knowledge as a supportive experiment.

Peer review and professional journals are still being found to be rife with bogus science. Dependence upon science for use as a source of Truth for use in a worldview is irrational. Scientism can never produce a true view of all reality. Moreover, this problem is already well known, and there is no sign of its abatement. Science is money driven and is always suspect, at least until it is too well empirically validated to warrant further questioning. Even then it is subject to further knowledge becoming available which negates the most trusted principles.