Tag: scientific method

The Chief Justice of the Supreme Court must act to restore real science to judicial oversight

Judges are not scientists, and the Supreme Court has never claimed they should resolve scientific debates. Their role is procedural and evidentiary: to demand that proffered “science” in court meets basic standards of reliability before it influences outcomes in toxic torts, regulatory challenges, or mass litigation. Post-Loper Bright, this gatekeeping is even more critical because agencies no longer get automatic deference on ambiguous statutes involving technical matters.

This is a timely and substantive call, rooted in a specific April 2026 letter from three distinguished physicists—Richard Lindzen (MIT emeritus), William Happer (Princeton emeritus), and Steven Koonin (Stanford Hoover Institution)—to Chief Justice John Roberts. They urge him, as chair of the Federal Judicial Center (FJC), to direct the removal of the new “How Science Works” chapter (65 pages) from the fourth edition of the Reference Manual on Scientific Evidence and reinstate the earlier, shorter, neutral version by David Goodstein (former Caltech vice provost).

The scientists argue that the replacement chapter shifts emphasis from the scientific method (testable predictions confronted with data, falsifiability, reproducible results) to “scientific consensus” and “community norms,” which they view as vulnerable to groupthink, funding pressures, and untestable claims—especially in fields like climate science where controlled experiments are limited. They tie this directly to the manual’s influence on thousands of judges handling over 1,000 climate-related cases and warn it risks undermining the Supreme Court’s 2024 Loper Bright decision, which ended Chevron deference and placed greater responsibility on judges to independently evaluate technical and scientific questions in statutory interpretation.

Judges are not scientists, and the Supreme Court has never claimed they should resolve scientific debates. Their role is procedural and evidentiary: to demand that proffered “science” in court meets basic standards of reliability before it influences outcomes in toxic torts, regulatory challenges, or mass litigation. Post-Loper Bright, this gatekeeping is even more critical because agencies no longer get automatic deference on ambiguous statutes involving technical matters.

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From CFACT

By Kevin Mooney, Jeff Reynolds

Calling Chief Justice John Roberts!

The latest edition of a scientific reference manual for judges across America is riddled with deficiencies that stem from potential conflicts of interest, politicized language, and assaults on the scientific method.

So says an April letter written to the Chief Justice of the U.S. Supreme Court by a group of professional climate scientists asking Roberts to intervene in edits made to the latest edition of judicial guidance on science issues.

Restoration News obtained a copy of the letter, penned by scientists with expertise in physics and atmospheric studies, who express serious concerns about the fourth edition of the Reference Manual on Scientific Evidence. The authors claim it departs from what they view as a long-standing practice of neutrality.

The scientists are asking Roberts to direct the Federal Judicial Center to remove one of the manual’s chapters and replace it with earlier version “that remains a concise, accurate, and non-ideological explanation for scientific reasoning appropriate for judicial use.” Otherwise, the scientists warn, the judiciary could be contaminated with faulty evidence.

This is no small administrative manner. In the wake of the 2024 Loper Bright decision by the Supreme Court that reversed the longstanding “Chevron Deference” doctrine, justices now have the freedom to interpret ambiguous laws, instead of deferring that interpretation to bureaucrats. Rather than relying on a federal agency to write its own rules in the face of such statutory ambiguity, judges can now rule whether federal regulations make any sense given the controlling statute.

The new rewrite of guidance to judges appears to constitute a deliberate sabotage of the Loper Bright ruling, once again boxing in judges. In this instance, however, the limitations require judges to consider “scientific consensus” OVER science determined by the scientific method.

This could have direct effects on hundreds of pending and future lawsuits by radical leftists who want to sue Big Oil in the manner they utilized to destroy Big Tobacco in the 1990s. Indeed, this appears to be a naked attempt to rig the game in favor of the “#ExxonKnew” campaign that seeks to hold petroleum companies liable for man-made global warming.

These lawsuits are projected to cause trillions of dollars’ worth of damage, not just to Big Oil, but the entire economy—which, after all, runs principally on oil and natural gas.

What Is the Federal Judicial Center?

Most Americans likely have no idea this agency even exists.

The Federal Judicial Center is the primary research and education agency for the United States federal courts, established by Congress in 1967. Located in Washington, D.C., it improves judicial administration by educating federal judges and staff, researching court operations, and preserving judicial history, all while operating without policymaking or enforcement authority.

As a separate agency within the judicial branch, the Federal Judicial Center has the congressionally assigned role of providing accurate, objective information to federal judges. By statute, the chief justice serves as the center’s chair.

Since the chapter in question in the Reference Manual on Scientific Evidence adopts what the letter’s signatories describe as an “advocacy framework,” as opposed to scientific one, they argue, it should be disqualified as reference material. The timing is critical as the letter says there are more than 1,000 climate-related cases working their way through state and federal courts. The Reference Manual is provided to more than 6,000 state and federal judges across America, making it a highly influential document since most judges are not trained in science.

Standing out from the pack is the litigation over the Trump administration’s decision to repeal the 2009 “endangerment finding” that declared CO2 a pollutant.

In February, EPA Administrator Lee Zeldin announced that his agency would reverse the Obama-era rule while saving American taxpayers more than $1.3 trillion in the process. The endangerment finding sits at the center of costly climate regulations that restrict consumer choice.

The problem began in 2007 when the U.S. Supreme Court ruled in Massachusetts v. EPA that greenhouse gases could qualify as pollutants under the Clean Air Act. In its 5–4 decision, the high court gave climate activists the greenlight they needed—via the EPA—to exert widespread control over the economy without congressional input.

Since the high court’s ideological complexion has changed since that time, litigation over Zeldin’s rule change could lead to a new ruling that overturns the 2007 decision. But that scenario will become less likely if federal judges do not have access to material that plays it straight with the science.

Even the Law Agrees: Consensus Is Not Science

In 1993, the Supreme Court established a precise definition of the scientific method in Daubert v. Merrell Dow Pharmaceuticals. The letter to Roberts points out that the third edition of the Reference Manual, published in 1994, relies on this ruling, as well as the long-established acceptance of the scientific method as the “gold standard” for scientific evidence.

But the manual’s new chapter on science conflicts with the scientific method. The emphasis the chapter places on “scientific consensus” and “widespread acceptance” conflicts with the scientific method because consensus is a sociological phenomenon not rooted in science. They write:

Consensus is an inferior and inherently fragile substitute for the gold-standard of science: testable predictions confronted with data. It is invoked primarily in fields where controlled experiments are difficult or impossible and where predictions cannot be decisively tested. It is vulnerable to groupthink, funding lock-in, and the natural human reluctance to acknowledge error.

The revised chapter uses an absurd flowchart to justify the use of consensus, on page 97, that stacks logical fallacies on top of each other like cordwood:

Just as bad is how the revised chapter “mischaracterizes” science as a community-governed enterprise rather than a quest for objective truth. “Science is indeed a human activity,” the letter says,” but its authority derives from reproducible results, not from community norms or majority agreement.”

Scientists Grounded in the Scientific Method

Three scientists signed the letter: Richard Lindzen, an emeritus professor of physics at the Massachusetts Institute of Technology; William Happer, an emeritus professor of physics at Princeton University; and Steven Koonin, the Edward Teller senior fellow at Stanford University’s Hoover Institution.

They credit the Federal Judicial Center for removing a chapter titled “Reference Guide on Climate Science” in response to correspondence from 28 state attorneys general that documented conflicts of interest and unsupported claims.

But here’s why there’s still a problem: A chapter underpinning the removed chapter remains in place. It is this massive chapter—titled “How Science Works”—that Lindzen, Happer, and Koonin want replaced with an earlier neutral and apolitical version:

Although the climate chapter has been removed, the chapter that undergirded it—”How Science Works”—remains. This chapter, 65 pages long, replaces the much shorter and widely respected 18-page version written for earlier editions by David Goodstein, former Vice Provost of Caltech. The new chapter does not acknowledge the prior version, nor does it resemble it in substance or tone.

The lead author of the new chapter, Professor Michael Weisberg, is a philosopher who also serves as a climate diplomat and advisor to several national delegations at the Conference of the Parties of the UN Framework Convention on Climate Change (COP) negotiations. His public biography highlights his work developing strategies to secure climate-related financial transfers for small island states. These roles do not inherently disqualify him. But hey do create a clear appearance of conflict when writing a chapter intended to guide judges on what constitutes legitimate scientific evidence—particularly in litigation where trillions of dollars may be at stake.

The director of the Federal Judicial Center has deleted the climate science chapter from its version of the manual.

But confusingly, the National Academy of Sciences will not remove the chapter from its printed and online versions of the same manual. Instead, the organization has opted for “asterisked language” to indicate that the Federal Judicial Center has omitted the chapter from its own version of the manual.

The resulting confusion could create vulnerabilities for activist judges to exploit in favor of environmental activists—perhaps deliberately.

Chief Justice Roberts could implement an immediate fix: Simply revert to the original version of “How Science Works,” which relied solely on the scientific method.

That fix would alleviate all confusion and guide judges toward ruling according to actual science—not agenda-driven political activism disguised as “scientific consensus.”

This article originally appeared at Restoration News

Muddling the Judiciary’s Understanding of Science

While an educational manual for federal judges was improved when a biased representation of climate change was removed, a remaining chapter on the fundamentals of science would poison the judiciary with quackery.

From CO2 COATITION

by Sharon Camp

While an educational manual for federal judges was improved when a biased representation of climate change was removed, a remaining chapter on the fundamentals of science would poison the judiciary with quackery.

The “How Science Works” chapter of the “Reference Manual on Scientific Evidence” allows for an overreliance on the unproven assumptions of computer models and an acceptance of “consensus” as proof even when contradicted by empirical evidence.

This violates tenets of the hundreds-year-old Scientific Method that require data obtained through experimentation or observation of the physical world as the means of supporting scientific conclusions. The undue regard for models and consensus is emblematic of climate alarmists whose claims fail scrupulous examination and whose views are shared by at least one of the chapter’s authors.

In defining the concepts of hypothesis, theory and scientific law, the writers exclude the elements of testing, observation and experimentation. Neither is it acknowledged that all three can be disproven, even though the ability to demonstrate the falseness of a conclusion has long been recognized as the path to advancing scientific knowledge.

Along these lines, the chapter even says that science cannot “disprove hypotheses,” although science has done so many times. A famous example is geocentricism that was supplanted in the 1500s by the heliocentric theory of Copernicus. Others include phrenology, eugenics, spontaneous generation and miasma theory of disease. All were embraced by much of the scientific community of their time – a consensus – until proven false. German scientist Alfred Wegener was dead for 30 years – and rejected by a consensus of his peers – before his theory of plate tectonics gained support in the last half of the 20th century.

The authors may misunderstand the Scientific Method. Or perhaps they believe that computer models relying on unproven assumptions that fail to explain or predict outcomes should be accepted over the extensive experimentation and observation required by the Scientific Method.

The chapter also misses the mark with this statement: “In the context of testing hypotheses, the term prediction refers to a logical consequence of a hypothesis, not necessarily what will happen in the future.” On the contrary, a prediction is all about what will happen in the future. Either objects of different weights dropped from the same height in a vacuum hit the ground at the same time or don’t. (They do.) A hypothesis also can be tested by determining whether it can explain something that has happened in the past.

Are the authors saying that a hypothesis paired with a model is sufficient confirmation, even when they fail to predict the future or explain the past? Or perhaps they believe that computer models predicting the climate 50-100 years in the future are more accurate than weather models that are reliable less than a week in advance — and sometimes failing that.

Apparently, there is no need to fret about such failures because the authors say: “The fact that there is room for improvement in the process of science does not necessitate distrust of hypotheses that have gained widespread acceptance in the scientific community and about which consensus has been achieved.” This is a justification for using consensus as a defense against evidence that challenges a popular narrative – a common ploy of climate alarmists.

In places, the chapter is self-contradictory. For example, the writers seem to embrace key elements of the Scientific Method when they say that a testable hypothesis must ultimately predict a set of observations that could or could not be made. This suggests a less cavalier relationship with traditional science than exhibited elsewhere, such as when key features of hypotheses and theories are omitted from their definitions. Unfortunately, the contradiction conveys a desire “to have it both ways” more than a commitment to rigorous scientific inquiry.

Ultimately, the chapter’s confusions and inordinate 62-page length muddy a reader’s understanding of the Scientific Method. More problematic, the writers introduce wrongheaded practices like the overuse of models and consensus in settling important questions. Such departures from the empirical tradition of Isaac Newton and Marie Curie make for superficial and specious investigations of reality known as pseudoscience.

Having received objections from state attorneys general and others, U.S. District Judge Robin Rosenberg excised the offending chapter on climate change from the reference manual. This is a remedy that needs to be applied by the National Academies of Sciences, Engineering and Medicine, which still includes the chapter on its website.

In the meantime, as head of the Federal Judicial Center, the manual’s publisher, Judge Rosenberg should do the same with “How Science Works.” The judiciary and the public it serves deserve nothing less.

Originally published in The Blaze on March 3, 2026.

Sharon Camp, senior education advisor for the CO2 Coalition, Fairfax, Virginia, has a Ph.D. in analytical chemistry, Georgia Institute of Technology, and has worked for the U.S. Environmental Protection Agency and as an advanced placement science teacher.

What’s New!

The CO2 Coalition has released its new episode of the Climate Debrief Podcast featuring Ron Stein. Watch the full video here!

The Matthew Effect, Mono-cultures, and the Natural Selection of Bad Science

Any politician faced with the challenge of protecting the public from a natural threat, such as a pandemic or climate change, will be keen to stress how much they are ‘following the science’ — by which they mean they are guided by the dominant scientific narrative of the day. We would want this to be the case because we trust the scientific method as a selective process that ensures bad science cannot hope to survive for very long. 

A group of men in black suits and ties standing in formation, facing the camera in a formal indoor setting.

From Climate Etc.

By John Ridgway

A group of medical professionals in white lab coats discussing around a table with books and notes, in front of a chalkboard filled with scientific diagrams and equations.

Any politician faced with the challenge of protecting the public from a natural threat, such as a pandemic or climate change, will be keen to stress how much they are ‘following the science’ — by which they mean they are guided by the dominant scientific narrative of the day. We would want this to be the case because we trust the scientific method as a selective process that ensures bad science cannot hope to survive for very long.  This is not a reality I choose to ignore here, but it is something I would certainly wish to place in its proper context. The problem is that the scientific method is not the only selector in town, and when all others are taken into account, a much murkier picture emerges – certainly not one that is clear enough to place a dominant narrative upon an epistemological pedestal.

Feedback reigns supreme

Of all the selection pressures that operate within a scientific community, perhaps the most fundamental is not the peer review of academic papers but one that can be summarised as follows:

“For to everyone who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.” (Matthew 25:29, RSV).

This is the so-called Matthew effect [1], also known as ‘cumulative advantage’. It is a positive feedback that serves to place fame and influence in the hands of a select few. This is true in life generally but also in academia specifically. For example, work that has already received a significant number of citations will tend to attract even more, if only because a currently large number of citations raises the likelihood of further reference resulting from any random selection from existing citation lists. This bibliometric phenomenon, in which success breeds success, was first studied by the physicist Derek de Solla Price, who emphasised its essentially stochastic properties:

“It is shown that such a stochastic law is governed by the Beta Function, containing only one free parameter, and this is approximated by a skew or hyperbolic distribution of the type that is widespread in bibliometrics and diverse social science phenomena.” [2]

In practice, however, the selection will be anything but random since factors such as influence and prestige will also determine the likelihood of one’s work being cited. Either way, the higher profile scientist will become even more successful.

The Matthew effect also has a bearing upon the chances of a paper being published in the first place. An editor or reviewer’s familiarity with the quality of an author’s existing canon of published work will make it easier to judge the latent merit of a submitted paper, improving that author’s chances of adding to his or her list of publications. A less well-known author will have no such advantage. This creates a positive feedback that can result in a mono-culture based upon the work of a relatively small number of dominant authors. Again, the Matthew effect may be purely statistical, requiring no particular bias or prejudice to be in evidence. As Remco Heesen and Jan-Willem Romeijn, the philosophers of science who have studied this effect, put it:

“This paper concerns biases that are rooted not in the prejudices of editors or reviewers, but rather in the statistical characteristics of editorial decision making…Hence, even if editors manage to purge their decision procedures of unconscious biases, they will be left with biases of a strictly statistical nature. The statistical biases contribute to the already existing tendency towards a mono-culture in science: a purely statistical Matthew effect.” [3]

There are in fact a number of ways in which mono-cultures may develop, each one involving the Matthew effect. For example, there is the feedback in which funding leads to success, which in turn leads to more funding. Also, a senior faculty member’s research interests will influence recruitment policy, thereby reinforcing faculty interest in those research areas [4]. Take, for example, the scientific mono-culture that quickly developed within the field of foundational physics. As physicist Lee Smolin explained back in 2006:

“The aggressive promotion of string theory has led to its becoming the primary avenue for exploring the big questions in physics. Nearly every particle physicist with a permanent position at the prestigious Institute for Advanced Study, including its director, is a string theorist; the exception is a person hired decades ago.” [5]

This dominance is not the fruit of the scientific method, since the vital element in which theories are tested experimentally is notable by its absence. This is not the case of a theory that ousted its contenders by proving to be more testable or by enjoying greater experimental verification. Its initial appeal stemmed from some early and quite spectacular theoretical successes, but string theory has since become mired in an arcane and thoroughly untestable set of mathematical conjectures that does not even qualify as a theory in the accepted sense. On the contrary, the ultimate dominance of string theory seems to be the result of positive feedbacks in which academic success became far more important than scientific achievement. In the words of Lee Smolin:

“Even as string theory struggles on the scientific side, it has triumphed within the academy.”

String theory’s rise to dominance is a classic case of what the Matthew effect can achieve when the scientific method is compromised. As such, it stands as a cautionary tale for any scientific field in which theorising and modelling ultimately outstrips the capacity for experimental confirmation.

Another of the problems with mono-cultures is that they can lead to a potentially unreliable narrative that acts as a societal beacon for normative thinking. As the narrative strengthens, and societal attitudes embed, so will the power to coerce a greater level of alignment within the scientific community. The consensus becomes a self-reinforcing social dynamic, for good or for bad. This is an example of a class of phenomena studied by organizational scientists Jörg Sydow & Georg Schreyögg:

“More often than not, organizations and also inter-organizational networks, markets or fields are characterized by dynamics that seem to run by and large beyond the control of agents…Among these mostly hidden and emergent dynamics, self-reinforcing processes seem of particular importance; they unfold their own dynamic, turning a possibly virtuous circle into a vicious one (Masuch, 1985).” [6]

Of course, no one who has been caught up in such a dynamic need assume the presence of subterfuge. That being said, the human race is no stranger to politicking and manipulation, and so bias and hoaxing remain optional extras. In particular, one has to be concerned that the growth of AI will increase the likelihood of problematic mono-cultures developing. As David Comerford, Professor of Economics and Behavioural Science, University of Stirling, points out:

“Just a few years ago it took months to produce a single paper. Now a single individual using AI can produce multiple papers that appear valid in a matter of hours.” [7]

Given that the Matthew effect is a numbers game, anything that can generate papers on an industrial scale must be of concern. And there is evidence that the trend is for such papers to be written by ghost writers in the services of corporate interest – so-called ‘resmearch’. As David Comerford explains:

“While the overwhelming majority of researchers are motivated to uncover the truth and check their findings robustly, resmearch is unconcerned with truth – it seeks only to persuade.”

And that is before considering the possibility that individuals may be using AI to increase their productivity so as to game the Matthew effect in their favour. Either way, AI has reduced the costs of producing such work to a virtual zero, thereby placing greater pressure upon the scientific method to counter the emergence of potentially unreliable mono-cultures.

The natural selection of bad science

Whilst mono-cultures are to be avoided, they won’t usually be centred upon bad science. Indeed, in science there is always a guiding hand designed to prevent this happening. Work is routinely evaluated by peers for its quality and value, and such scrutiny should be to the benefit of the good science. Except the evidence would seem to suggest that bad science can still thrive despite such scrutiny. There is another selection operating, but far from acting as a corrective force, filtering out poor work and removing both purely statistical and bias-fuelled positive feedbacks, it seems to be one that can actually promote bad science. The explanation for this troublesome effect has been given by Paul E. Smaldino and Richard McElreath. The opening statement of their paper’s abstract sets the scene:

“Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing—no deliberate cheating nor loafing—by scientists, only that publication is a principal factor for career advancement.” [8]

The poor research design and data analyses alluded to here are the misuse of p-values and variations on the theme of data torturing, all of which have been widespread within the behavioural sciences for many years now. The problem arises because publication provides the primary method of reward, and yet publication requires positive results, which in turn encourages practices that lead to false positives. Richard Horton, editor of The Lancet, points towards the need to have the right incentives:

“Part of the problem is that no one is incentivised to be right. Instead, scientists are incentivised to be productive and innovative.” [9]

Smaldino and McElreath emphasise that no stratagem is required:

“This paper argues that some of the most powerful incentives in contemporary science actively encourage, reward and propagate poor research methods and abuse of statistical procedures. We term this process the natural selection of bad science to indicate that it requires no conscious strategizing nor cheating on the part of researchers. Instead, it arises from the positive selection of methods and habits that lead to publication.”

They continue by pointing out the obvious fact that “Methods which are associated with greater success in academic careers will, other things being equal, tend to spread”. One would like to think that only the good practices spread, but this is clearly not the case. The ones that spread are the ones that are associated the most with career success, and this unfortunately involves a set of criteria that only partially correlates with quality of work. In this instance, the weaker the statistical power of the data, the greater the chances of publication — and publication is what everyone seems to want.

Fortunately, this is not a problem in which the scientific method stands idly by. Replication and reproducibility are its cornerstones and, as a consequence, the malpractice has manifested itself in the infamous ‘reproducibility crisis’ within science. Opinions differ regarding how serious the problem is; some would claim that the crisis is existential whilst others feel the problem is somewhat overstated. However, no one is claiming that the problem is easily rectifiable, which is unsurprising given that the problem has its roots in the reward structures that sustain academia [10].

So where does that leave us?

The social structures and reward mechanisms within science are such that both good and bad science can find itself the beneficiary of a natural selection, and it can be very difficult for the lay person to know which way the selection has operated when creating a dominant narrative. Knowing the strength of consensus is not nearly as important as understanding the mechanisms at play, and to assume that they are dominated by the scientific method is naïve. Add to that the statistical effects that predispose academia to the emergence of potentially damaging mono-cultures, and one has further reason to resist the temptation to automatically accept the narrative of the day.

It should be appreciated, however, that this is not an anti-science sentiment. It is precisely because social dynamics can entrench ideas irrespective of their epistemological validity that the scientific method is so important. Nevertheless, a mature appreciation of the importance of the scientific approach should entail an understanding that the scientific method cannot hope to be one hundred percent effective in eradicating the vagaries and stochastics of consensus building. In particular, it cannot hope to fully avoid the effects of statistical Matthew effects and their predisposition to create mono-cultures. A mature appreciation of the importance of the scientific approach should therefore include also an understating that there is really no need to invoke the idea of a scientific subterfuge. There is no conspiracy, only scientists doing their job.

Footnotes:

[1] The term was first coined in the context of the sociology of science by Robert K. Merton and Harriet Anne Zuckerman. See Merton R.K. 1968 “The Matthew effect in science”, Science, New Series, Vol 159, No. 3810, pp. 56-63. https://repo.library.stonybrook.edu/xmlui/bitstream/handle/11401/8044/mertonscience1968.pdf?sequence=1&isAllowed=y.

[2] de Solla Price, Derek J. 1976, “A general theory of bibliometric and other cumulative advantage processes”, J. Amer. Soc. Inform. Sci., 27 (5): 292–306, https://doi.org/10.1002/asi.4630270505.

[3] Heesen R., Romeijn JW. 2019 “Epistemic Diversity and Editor Decisions: A Statistical Matthew Effect”, Philosophers’ Imprint, Vol. 19, No. 39, pp. 1-20. http://hdl.handle.net/2027/spo.3521354.0019.039.

[4] In fact, respect for senior faculty members has a lot to answer for when it comes to establishing consensus. See Perret C. and Powers S. T. 2022, “An investigation of the role of leadership in consensus decision-making”, Journal of Theoretical Biology, Vol 543, 111094, https://doi.org/10.1016/j.jtbi.2022.111094.

[5] Smolin L. 2006 “The Trouble With Physics”, page xx, ISBN 978-0-141-01835-5.

[6]  Sydow, J., Schreyögg, G. 2013 “Self-Reinforcing Processes in Organizations, Networks, and Fields — An Introduction”. In: Sydow, J., Schreyögg, G. (eds) Self-Reinforcing Processes in and among Organizations. Palgrave Macmillan, London. https://doi.org/10.1057/9780230392830_1.

[7] Comerford D. 2025 “We risk a deluge of AI-written ‘science’ pushing corporate interests – here’s what to do about it”. The Conversation. https://theconversation.com/we-risk-a-deluge-of-ai-written-science-pushing-corporate-interests-heres-what-to-do-about-it-264606.

[8] Smaldino P.E., McElreath R. 2016 “The natural selection of bad science”, R. Soc. Open Sci., 3: 160384, http://doi.org/10.1098/rsos.160384.

[9] Horton R. 2015 “Offline: What is medicine’s 5 sigma?”, The Lancet, Volume 385, Issue 9976 p1380. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60696-1/fulltext.

[10] Leyser O., Kingsley D., Grange J. 2017, “Opinion: The science ‘reproducibility crisis’ – and what can be done about it”. University of Cambridge – Research News. https://www.cam.ac.uk/research/news/opinion-the-science-reproducibility-crisis-and-what-can-be-done-about-it.

We teach kids only half the scientific method

The vigilant folks at the CO2 Coalition have been saying we are no longer teaching the scientific method in public school science education classes. This puzzled me because the new rules for science education claim to teach kids to think like scientists. I figured the kids would be awash with scientific method.

From CFACT

By David Wojick


13425083 – happy teenage middle school or highschool students in class.

The vigilant folks at the CO2 Coalition have been saying we are no longer teaching the scientific method in public school science education classes. This puzzled me because the new rules for science education claim to teach kids to think like scientists. I figured the kids would be awash with scientific method.

I was wrong. With the help of some Coalition experts, I investigated this strange situation and here are my basic findings.

In a nutshell, which is explained more fully below, only the happy half of the scientific method is being taught. This is the fun formulation of possible hypotheses and models that might explain what we observe. The hard half, where these tentative explanations get evaluated and likely fail, is not taught.

Now, let’s look at it in some detail. It is all about what are called “standards” which are actually State regulations saying what topics will be taught at each grade level. About two decades ago, there began a push for national standards because while most states taught the same topics, they were often in different grades, which created problems –textbooks, for example.

The Feds paid the States to adopt Common Core standards in math and English, but they punted science to the National Academy of Sciences. Agencies often did this with science issues.

In 2012, the NAS produced its blueprint for science standards — “A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas”, which is often simply called the Framework.

A bunch of State people then formed a huge working group that wrote standards to fit the Framework. These are called the “Next Generation Science Standards,” or NGSS for short. Today, 20 States have officially adopted the NGSS, while another 29 are reported to have standards that are based on the Framework. (The sole holdout is Florida.)

The Framework and NGSS are very different from the pre-existing State science standards. These older standards taught scientific knowledge, basically the fundamental facts that underlie each of the primary scientific areas. Electricity, cell structure, the solar system, and stuff like that.

Some of this is still taught as the Core Ideas, plus there are Crosscutting Concepts like causality. But the method is what is called Practices. These are supposed to be how scientists work, plus they have added engineering. Students do projects instead of learning facts.

Here are the listed practices from the Framework (and the NGSS):

1. Asking questions (for science) and defining problems (for engineering)

2. Developing and using models

3. Planning and carrying out investigations

4. Analyzing and interpreting data

5. Using mathematics and computational thinking

6. Constructing explanations (for science) and designing solutions (for engineering)

7. Engaging in argument from evidence

8. Obtaining, evaluating, and communicating information

Note that the list basically is sequential. It ends with formulating, arguing for, and communicating explanations.

There is nothing about testing, evaluating, or criticizing these explanations. But this sort of critical evaluation is the essence of science. It sets science apart from unconstrained belief. This is the very heart of the scientific method, so the CO2 Coalition is dead right.

Then, it gets very puzzling because the Framework actually presents evaluation as a central activity. The Practices list is Box 3.1 in the Framework. Just three pages later, we find Figure 3.1 which looks to be a diagram version and is labeled “The three spheres of activity for scientists and engineers.”

The central sphere is labeled “Evaluating” and lists “ARGUE, CRITIQUE, and ANALYZE” as its activities. There is an arrow pointing directly to “FORMULATE HYPOTHESES” under the heading “Developing Explanations.”

I have no idea why evaluating proposed explanations (and models) was not included in the list of practices. Perhaps they simply wanted to make science fun by avoiding the unpleasant part. After all, progressives are often against grading students and especially against failing them.

But whatever the reason, the Framework and NGSS only teach the happy half of the scientific method. The crucial part where hypothesis meets reality is not there. So, they are actually failing to teach how scientists think and work.

I propose a simple fix taken right from the Framework. The NGSS and the States should simply add this activity to the list:

“Activity 9: Proposed models and explanations will be evaluated using analysis, critique, and argument from evidence.”

Now we just have to get this out to the 49 States.