The thinking error at the root of science denial

Could seeing things in black-and-white terms influence people’s views on scientific questions? Lightspring/Shutterstock.com

Jeremy P. Shapiro, Case Western Reserve University

Currently, there are three important issues on which there is scientific consensus but controversy among laypeople: climate change, biological evolution and childhood vaccination. On all three issues, prominent members of the Trump administration, including the president, have lined up against the conclusions of research.

This widespread rejection of scientific findings presents a perplexing puzzle to those of us who value an evidence-based approach to knowledge and policy.

Yet many science deniers do cite empirical evidence. The problem is that they do so in invalid, misleading ways. Psychological research illuminates these ways.

No shades of gray

As a psychotherapist, I see a striking parallel between a type of thinking involved in many mental health disturbances and the reasoning behind science denial. As I explain in my book “Psychotherapeutic Diagrams,” dichotomous thinking, also called black-and-white and all-or-none thinking, is a factor in depression, anxiety, aggression and, especially, borderline personality disorder.

In this type of cognition, a spectrum of possibilities is divided into two parts, with a blurring of distinctions within those categories. Shades of gray are missed; everything is considered either black or white. Dichotomous thinking is not always or inevitably wrong, but it is a poor tool for understanding complicated realities because these usually involve spectrums of possibilities, not binaries.

Spectrums are sometimes split in very asymmetric ways, with one-half of the binary much larger than the other. For example, perfectionists categorize their work as either perfect or unsatisfactory; good and very good outcomes are lumped together with poor ones in the unsatisfactory category. In borderline personality disorder, relationship partners are perceived as either all good or all bad, so one hurtful behavior catapults the partner from the good to the bad category. It’s like a pass/fail grading system in which 100 percent correct earns a P and everything else gets an F.

In my observations, I see science deniers engage in dichotomous thinking about truth claims. In evaluating the evidence for a hypothesis or theory, they divide the spectrum of possibilities into two unequal parts: perfect certainty and inconclusive controversy. Any bit of data that does not support a theory is misunderstood to mean that the formulation is fundamentally in doubt, regardless of the amount of supportive evidence.

Similarly, deniers perceive the spectrum of scientific agreement as divided into two unequal parts: perfect consensus and no consensus at all. Any departure from 100 percent agreement is categorized as a lack of agreement, which is misinterpreted as indicating fundamental controversy in the field.

There is no ‘proof’ in science

In my view, science deniers misapply the concept of “proof.”

Proof exists in mathematics and logic but not in science. Research builds knowledge in progressive increments. As empirical evidence accumulates, there are more and more accurate approximations of ultimate truth but no final end point to the process. Deniers exploit the distinction between proof and compelling evidence by categorizing empirically well-supported ideas as “unproven.” Such statements are technically correct but extremely misleading, because there are no proven ideas in science, and evidence-based ideas are the best guides for action we have.

I have observed deniers use a three-step strategy to mislead the scientifically unsophisticated. First, they cite areas of uncertainty or controversy, no matter how minor, within the body of research that invalidates their desired course of action. Second, they categorize the overall scientific status of that body of research as uncertain and controversial. Finally, deniers advocate proceeding as if the research did not exist.

For example, climate change skeptics jump from the realization that we do not completely understand all climate-related variables to the inference that we have no reliable knowledge at all. Similarly, they give equal weight to the 97 percent of climate scientists who believe in human-caused global warming and the 3 percent who do not, even though many of the latter receive support from the fossil fuels industry.

This same type of thinking can be seen among creationists. They seem to misinterpret any limitation or flux in evolutionary theory to mean that the validity of this body of research is fundamentally in doubt. For example, the biologist James Shapiro (no relation) discovered a cellular mechanism of genomic change that Darwin did not know about. Shapiro views his research as adding to evolutionary theory, not upending it. Nonetheless, his discovery and others like it, refracted through the lens of dichotomous thinking, result in articles with titles like, “Scientists Confirm: Darwinism Is Broken” by Paul Nelson and David Klinghoffer of the Discovery Institute, which promotes the theory of “intelligent design.” Shapiro insists that his research provides no support for intelligent design, but proponents of this pseudoscience repeatedly cite his work as if it does.

For his part, Trump engages in dichotomous thinking about the possibility of a link between childhood vaccinations and autism. Despite exhaustive research and the consensus of all major medical organizations that no link exists, Trump has often cited a link between vaccines and autism and he advocates changing the standard vaccination protocol to protect against this nonexistent danger.

There is a vast gulf between perfect knowledge and total ignorance, and we live most of our lives in this gulf. Informed decision-making in the real world can never be perfectly informed, but responding to the inevitable uncertainties by ignoring the best available evidence is no substitute for the imperfect approach to knowledge called science.

Jeremy P. Shapiro, Adjunct Assistant Professor of Psychological Sciences, Case Western Reserve University

This article was originally published on The Conversation. (Reblogged by permission). Read the original article.

The problem of false balance when reporting on science

Peter Ellerton, The University of Queensland

How do you know the people billed as science experts that you see, hear and read about in the media are really all that credible? Or have they been included just to create a perception of balance in the coverage of an issue?

It’s a problem for any media and something the BBC’s Trust is trying to address in its latest report on science impartiality in programming.

As part of ongoing training, staff, particularly in non-news programs, were told that impartiality is not just about including a wide range of views on an issue, as this can lead to a “false balance”. This is the process of providing a platform for people whose views do not accord with established or dominant positions simply for the sake of seeming “balanced”.

The BBC has been criticised before for “false balance” and there are reports now that certain climate change sceptics are banned from BBC News, although this is denied by the BBC.

It’s understandable that such false balance could grow from a desire to seem impartial, and particularly so since public broadcasters such as the BBC and the ABC in Australia are sensitive to claims of imbalance or bias.

Couple this with the need to negotiate the difficult ground of expert opinion, authentic balance and audience expectation, not to mention the always delicate tension between the imperatives of news and entertainment, and it hardly seems surprising that mistakes are made. An investigation this year found the ABC breached its own impartiality standards in its Catalyst program last year on statins and heart disease.

Finding the right balance

How then can journalists decide the best way to present a scientific issue to ensure accurate representation of the views of the community of experts? Indeed, how can any of us determine if what we are seeing in the media is balanced or a misrepresentation of expert opinion?

Hard to find the right balance.
Flickr/Paxson Woelber , CC BY

As I have written elsewhere, it is important to not confuse the right to be heard with an imagined right to be taken seriously. If an idea fails to survive in the community of experts, its public profile should diminish in proportion to its failure to generate consensus within that community.

A common reply to this is that science isn’t about consensus, it’s about the truth. This is so, but to use a consensus as evidence of error is fallacious reasoning.

While it’s true that some presently accepted notions have in the past been peripheral, the idea that simply being against the majority view equates to holding your intellectual ground in the best tradition of the enlightenment is ludicrous.

If all views are equal, then all views are worthless.

Were I to propose an idea free of testing or argument, I could not reasonably expect my idea to be as credible as those subject to rigorous experimentation and collaborative review. If such equality did exist then progress would be impossible, since progress is marked by the testing and rejection of ideas.

Defining an expert

In the case of science, this testing is the process of experimentation, data analysis and peer review. So if someone – scientist or otherwise – has not worked and published in an area, then they are not an expert in that area.

The first imperative for a journalist covering any story is to determine exactly in what field the issue best sits and then to seek advice from people who work and publish in that field.

Knowing how the issue fits into the broader picture of scientific investigation is very useful in determining this. It is one of the reasons that good science journalism follows from having journalists with some training in science.

Such a selection process, performed transparently, is an excellent defence against charges of bias.

Avoiding false balance

False balance can also be created by assuming that a person from outside the field (a non-expert) will somehow have a perspective that will shed light on an issue, that the real expert is too “caught up in the details” to be objective.

But suggesting that an expert is naive usually indicates an attempt at discrediting rather than truth seeking. Credibility is more about process than authority, and to be a recognised expert is to work within the process of science.

Also, if a piece of science is being criticised, we should ask if the criticism itself has been published. It’s not enough that someone with apparent authority casts doubt as this is simply an appeal to authority – an appeal that critics of mainstream science themselves use as a warrant to reject consensus.

A second journalistic imperative would be to recognise that not all issues are binary.

Coins may have two sides but not so every science issue.
Flickr/monkeyc net, CC BY-NC-SA

The metaphor that a coin has two sides is a powerful one, and the temptation to look at both sides of an issue is naturally strong. But the metaphor also assumes an equal weighting, and that both sides present the same space for discussion.

Proof and evidence

When an issue is genuinely controversial, the burden of proof is shared between opposing views. When a view is not mainstream, say that scientists are engaged in a conspiracy to defraud the public, the burden of proof sits with those promoting that view.

In such cases, as Christopher Hitchens succinctly put it:

What can be asserted without evidence can also be dismissed without evidence.

Attempting to dishonestly shift the burden of proof is a common device in the push to have young earth creationism taught in science classrooms.

The idea of “teaching both sides” or that students should be allowed to make up their own minds seems again like a recourse to the most basic ideas of a liberal education, but is in reality an attempt to bypass expert consensus, to offload the burden of proof rather than own it.

The fact is, that for issues such as creationism, vaccination and that climate change is occurring and is a function of human activity, it’s not about journalists suppressing views, it’s about quality control of information.

Stay with the issue

A classic means of muddying the waters is to employ straw man arguments, in which the point at issue is changed to one more easily defended or better suited to a particular interest. Politicians are adept at doing this, dodging hard questions with statements like “the real issue is” or “what’s important to people is”.

An expert versus who?

Deniers of climate science often change the issue from global warming to whether or not consensus is grounds for acceptance (it alone is not, of course), or focus on whether a particular person is credible rather than discuss the literature at large.

The anti-vaccine lobby talks about “choice” rather than efficacy of health care.
Young earth creationists talk about the right to express all views rather than engage with the science. Politicians talk about anything except the question they were asked.

The third imperative, therefore, is to be very clear as to what the article or interview is about and stick to that topic. Moving off topic negates the presence of the experts (the desired effect) and gives unsubstantiated claims prominence.

The impartiality checklist

The best method of dealing with cranks, conspiracy theorists, ideologues and those with a vested interest in a particular outcome is the best method for science reporting in general:

• insist on expertise
• recognise where the burden of proof sits
• stay focused on the point at issue.

If the media sticks to these three simple rules when covering science issues, impartiality and balance can be justifiably asserted.

Correction: This article was amended on July 17, 2014 to include a report of the BBC’s denial that a climate change sceptic was banned from the public broadcaster.

Peter Ellerton, Lecturer in Critical Thinking, The University of Queensland

This article was originally published on The Conversation. (Reblogged by permission). Read the original article.

 

Listen and learn: the language of science and scepticism

Peter Ellerton, The University of Queensland

As scientists, one of our responsibilities should be to promote clarity. A lot of problems are caused by an incorrect or incomplete understanding of terms we regularly, and even lovingly, use.

When I use the word “evidence”, what I think I mean is a function of many things, not least my education in science and philosophy.

It’s also the product of many discussions with people about science, superstition, psychology, pseudoscience and subjectivity.

These discussions have added nuance to my understanding of the nature of evidence. They’ve also alerted me to the fact this nature changes in certain circumstances and through certain worldviews. In other words, what I intend to say is sometimes heard as something else entirely.

This type of miscommunication can be bad enough when dealing with someone who isn’t using the terms in a scientific way, but it’s particularly frustrating when it happens when talking to teachers and communicators of science.

I’d like to take a shot, then, at defining some key terms in the name of clarity.

P Shanks

Scientific law

People might think scientific law is about the highest sort of truth you can get; they might think something “proven” scientifically has the status of certainty, which is to say it’s always true: nature will always behave so as to be in accord with this law.

While in some way accurate, that interpretation is fundamentally flawed. It conflates (or worse, ignores) important concepts and creates a brittleness in the public conception of science that erodes confidence and trust.

First and foremost, laws in science are seldom proven: they are demonstrated, and they are demonstrated because they are demonstrable, which is to say they are descriptive.

Newton’s inverse square law of gravity outlines how the force of gravity between two massive objects varies with distance. Basically, if you double the distance, the force is reduced by a factor of four. Triple it and the force reduces by a factor of nine, and so on.

The same relationship with distance holds for the intensity of omnidirectional radiation, as shown below. What’s significant about a law like this is that while it describes the effect it does not really explain it.

 

Newton himself was famously silent on the question of what gravity was and why it would behave this way. To get an explanation of what gravity is, we needed Einstein. And we needed a theory.

Modelling reality

General relativity explains the phenomena associated with gravity by postulating that the presence of mass warps, and hence affects movement through, space-time. This theory – or model – of how the universe works, when “run” through the process of mathematical calculation, produces outcomes that correspond to possible states of the world.

These states are checked against reality to test their veracity. The more times the model produces results that agree with observation, the more confidence we have in the model as an accurate representation of how the world works.

The example above shows nicely the difference between a model and a law: the former is a representation of reality, the latter a descriptive account.

It’s worth noting, of course, that “model” can be both a noun and a verb (and sometimes both at once). We can build a model of the solar system, or we can model weather on a computer. Either way, the terminology holds.

To put this another way, a law describes what happens and to what degree, but if we want to find out why it happens we need a theory – a model that represents reality.

A model can give us a more satisfying insight into the possible mechanisms of the universe – it’s an analogy (for rarely is it completely accurate) that betters our comprehension, as analogies are designed to do.

Both theories and laws have predictive power and are subject to being refining, falsified or confirmed; although in the case of laws refining is best done in the light of theoretical change (i.e. explaining the law by the theory/model).

Observing the law

We generalise to laws through observation, and support our generalisations with theoretical understanding. But it can be very tricky to determine that something is true in all cases (we can’t test the potential law in all possible places and at all possible times) or just happens to be true every time we check.

When stating something is universally true (even if parameters need to be defined), we must be very careful to determine whether we mean it’s true because it must be that way, or simply because it happens to be that way.

It may be a necessary condition of the universe that all like charges repel each other. But what about a generalisation such as “all posters are held up by drawing pins”?

The posters in my room and all those in my building are held up by drawing pins, but this hardly seems a necessary condition of posters: surely something else would do the job just as well. These are extreme examples, but many “laws” of nature may not be necessary laws – which seems to suggest they really shouldn’t be called laws in the first place.

Calling something a law certainly does not mean it is unchallengeable.

Laws do not develop from theories. To put it another way, theories do not become laws. I have thrown out science textbooks from several schools because they outline an unrealistic progression: from hypothesis to theory to law.

These three concepts are different creatures, and one does not morph into the other. One of the most significant misunderstandings in science exists because of this type of thinking.

Certainty

In as much as science can make us sure of anything, we are sure evolution occurred in the manner generally accepted by evolutionary biologists; it is a fact about the world.

Darwin, as is generally known, developed a theory – a model – to explain evolution. This model is natural selection. It’s unfortunate that the lovely phrase “the theory of evolution by natural selection” has been truncated into the misleading, inaccurate, confusing and very wrong phrase “the theory of evolution” – including on this very website.

The “theory of evolution” is wrong for two reasons (when scientists use it they know of what they speak, but this is not my point). First, evolution is not the model – natural selection is. So we immediately conflate two very different ideas – that of evolution and the model of natural selection.

When added to the mistaken belief that theories become laws, adherents of young earth creationism (for there are really no other serious evolution opposers) can claim evolution as a tentative conclusion, akin to vague, hand-waving notions, that culminated in Ronald Reagan’s famous dismissal of evolution as “only a theory”.

The consequences for both the teaching of evolution and the credibility of science are enormous. And yet I have never seen a defender of science articulate this misunderstanding.

Joshfassbind.com

Hypothesis

Just as a theory is a model, and law is a generalisation, a hypothesis is a statement about the world that could be true or false.

Moreover, the statement must be testable, which means it must be falsifiable, or inherently disprovable.

Phrased like this, hypotheses seem to have more in common with laws than they do with theories, considering that Newton could easily have hypothesised the inverse square law of gravity without going through any theoretical modelling of gravity.

But, of course, the creative act of devising a model of the universe, or a part of it, is to hypothesise that the world is really like that, and the hypothesis becomes that the model is an accurate representation.

Hypotheses, then, are ways of talking about building theories and laws, but not in the common way of theories being intermediate between hypotheses and laws.

While hypotheses can stand alone or inform both theories and laws, the interplay in practice between various hypotheses, theories and laws is web-like and complex and exists at nearly every level of operation from the experiment of the day to the paradigm of the century.

The idea of a hypothesis-to-theory-to-law progression is seriously flawed, and this needs to be articulated as the root cause of much misunderstanding.

Proof

“Prove” comes from the Latin probare, meaning “to test”. It’s also the origin of the word “probe”.

An older term – “proving ground” – for a testing area or trial shows we have not entirely lost that interpretation. But in the everyday use of the term, “proof” has come to indicate certitude.

AJC1

What remains poorly understood is that “proof”, as such, is a deductive creature that really does not sit comfortably in science (at least not in an affirming sense). In mathematics a proof conveys that, within the bounds of the axioms in use, there is a truth to be discovered or a certainty to be expressed.

For its theoretical claims, and indeed for its laws, inductive science can only boast confirming instances.

Headlines that (routinely) claim “Einstein proved right“ would, we know from his own words, make the great man turn in his grave.

He often spoke of the exquisite sensitivity of his theories to falsification, saying that it would not matter how many times experiment agreed with him, it had only to disagree once to prove him wrong (granted, of course, the validity of the experiment, as recent neutrino-based dramas have shown).

The simple fact that we can never test his theories under all conditions in all places at all times creates conclusions that are tentative, even though the level of confidence may be very high.

We may “prove” facts about the world, such as Earth being more or less spherical, but this does not extend to our laws and theories to the extent we might like to think.

So proof works best in science to falsify, not to affirm, though this is the opposite of common belief.

If we are clear on the above, we have a better appreciation of what makes an idea scientific, as opposed to pseudo-scientific.

We know that the best scientific hypotheses and theories are those with great explanatory power and high sensitivity to falsification, and that these are often the results of highly creative thinking, as are the experimental attempts to confirm or falsify them.

This is a very beautiful idea, but one that can’t be appreciated unless you know science does not spend its time stamping into place dry facts about the world, but grows as a vigorous and exhilarating human enterprise showcasing the best of collective human achievement.

Clarifying these ideas will, I hold, go a very long way indeed into increasing people’s understanding of science and their confidence in scientific findings.

Peter Ellerton, Lecturer in Critical Thinking, The University of Queensland

This article was originally published on The Conversation. (Reblogged by permission). Read the original article.

 

Where’s the proof in science? There is none

By Geraint Lewis, University of Sydney

UNDERSTANDING RESEARCH: What do we actually mean by research and how does it help inform our understanding of things? Those people looking for proof to come from any research in science will be sadly disappointed.

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As an astrophysicist, I live and breathe science. Much of what I read and hear is couched in the language of science which to outsiders can seem little more than jargon and gibberish. But one word is rarely spoken or printed in science and that word is “proof”. In fact, science has little to do with “proving” anything.

These words may have caused a worried expression to creep across your face, especially as the media continually tells us that science proves things, serious things with potential consequences, such as turmeric can apparently replace 14 drugs, and more frivolous things like science has proved that mozzarella is the optimal cheese for pizza.

Surely science has proved these, and many other things. Not so!

The way of the mathematician

Mathematicians prove things, and this means something quite specific. Mathematicians lay out a particular set of ground rules, known as axioms, and determine which statements are true within the framework.

A statue of Euclid with something very interesting added to his scroll. (Source: Garrett Coakley)

One of best known of these is the ancient geometry of Euclid. With only a handful of rules that define a perfect, flat space, countless children over the last few millenia have sweated to prove Pythagoras’s relation for right-angled triangles, or that a straight line will cross a circle at most at two locations, or a myriad of other statements that are true within Euclid’s rules.

Whereas the world of Euclid is perfect, defined by its straight lines and circles, the universe we inhabit is not. Geometrical figures drawn with paper and pencil are only an approximation of the world of Euclid where statements of truth are absolute.

Over the last few centuries we’ve come to realise that geometry is more complicated than Euclid’s, with mathematical greats such as Gauss, Lobachevsky and Riemann giving us the geometry of curved and warped surfaces.

In this non-Euclidean geometry, we have a new set of axioms and ground-rules, and a new set of statements of absolute truth we can prove.

These rules are extremely useful for navigating around this (almost-)round planet. One of Einstein’s (many) great achievements was to show that curving and warping spacetime itself could explain gravity.

Yet, the mathematical world of non-Euclidean geometry is pure and perfect, and so only an approximation to our messy world.

Just what is science?

But there is mathematics in science, you cry. I just lectured on magnetic fields, line integrals and vector calculus, and I am sure my students would readily agree that there is plenty of maths in science.

Albert Einstein. (Source: Wikimedia/Doris Ulmann)

And the approach is same as other mathematics: define the axioms, examine the consequences.

Einstein’s famous E=mc², drawn from the postulates of how the laws of electromagnetism are seen by differing observers, his special theory of relativity, is a prime example of this.

But such mathematical proofs are only a part of the story of science.

The important bit, the bit that defines science, is whether such mathematical laws are an accurate description of the universe we see around us.

To do this we must collect data, through observations and experiments of natural phenomena, and then compare them to the mathematical predictions and laws. The word central to this endeavour is “evidence”.

The scientific detective

The mathematical side is pure and clean, whereas the observations and experiments are limited by technologies and uncertainties. Comparing the two is wrapped up in the mathematical fields of statistics and inference.

Many, but not all, rely on a particular approach to this known as Bayesian reasoning to incorporate observational and experimental evidence into what we know and to update our belief in a particular description of the universe.

The only way is down for these apples.
(Source: Flickr/Don LaVange)

Here, belief means how confident you are in a particular model being an accurate description of nature, based upon what you know. Think of it a little like the betting odds on a particular outcome.

Our description of gravity appears to be pretty good, so it might be odds-on favourite that an apple will fall from a branch to the ground.

But I have less confidence that electrons are tiny loops of rotating and gyrating string that is proposed by super-string theory, and it might be a thousand to one long-shot that it will provide accurate descriptions of future phenomena.

So, science is like an ongoing courtroom drama, with a continual stream of evidence being presented to the jury. But there is no single suspect and new suspects regularly wheeled in. In light of the growing evidence, the jury is constantly updating its view of who is responsible for the data.

But no verdict of absolute guilt or innocence is ever returned, as evidence is continually gathered and more suspects are paraded in front of the court. All the jury can do is decide that one suspect is more guilty than another.

What has science proved?

In the mathematical sense, despite all the years of researching the way the universe works, science has proved nothing.

Mark the spot where nothing was proved. (Source: Flickr/Rob)

Every theoretical model is a good description of the universe around us, at least within some range of scales that it is useful.

But exploring into new territories reveals deficiencies that lower our belief in whether a particular description continues to accurately represent our experiments, while our belief in alternatives can grown.

Will we ultimately know the truth and hold the laws that truly govern the workings of the cosmos within our hands?

While our degree of belief in some mathematical models may get stronger and stronger, without an infinite amount of testing, how can we ever be sure they are reality?

I think it is best to leave the last word to one of the greatest physicists, Richard Feynman, on what being a scientist is all about:

“I have approximate answers and possible beliefs in different degrees of certainty about different things, but I’m not absolutely sure of anything.”

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This article is part of a series on Understanding Research.

Further reading:
Why research beats anecdote in our search for knowledge
Clearing up confusion between correlation and causation
Positives in negative results: when finding ‘nothing’ means something

Geraint Lewis receives funding from the Australian Research Council, including a Future Fellowship.

This article was originally published on The Conversation.
Read the original article.