Being systematic, rigorous and quantitative, science is a good way to pursue knowledge. However, it is limited to a specific philosophical framework. The philosophy of science defines its limits, methods and presuppositions. Being the foundation of critical thinking, it’s a good way of investigating and testing our beliefs about the natural word, and more broadly what humans can know. Because humans are plagued with flaws in cognition, perception and memory, a system of methods was developed to compensate for those failings.
Epistemology
We turn now to Epistemology, the philosophy that deals with the nature of human knowledge, but also sets the limits of what’s knowable. There are many questions science cannot answer because they are scientifically unanswerable, as opposed to factual questions. Although many might not think in these terms, it’s hard to avoid doing philosophy in science.
Empiricism states that knowledge comes from sensory experience. John Locke is considered the first empiricist. He was concerned about the boundaries of human knowledge, and claimed that thought comes from experience, that experience is the essence of thought and stated:
“Nothing is in the mind that was not first in the senses.”
Locke recognized the limitations of sensory experience, and therefore the limitations of human knowledge.
The Epistemological Limits of Science
Science only deals with scientific claims, any claim can be scientific if it is practically testable and falsifiable. If the notion is testable it involves science. Science works within the philosophical framework of science. If all of the assumptions of science are correct, they should produce some objective, positive results overtime.
Pragmatism
William James and John Dewey developed a comprehensive philosophy based on a “scientific” response to experience. For them, universal knowledge of what lies beyond experience is impossible. Dewey understood Pragmatism as prospective empiricism, and that science should be a pragmatic belief system. Pragmatism was a philosophy built on an experience-based conception of scientific knowledge.
Logical positivism
Later came the logical positivists. For them, science should be adequately reconstructed in empiricist terms, as a linguistic kind of thing with a set of sentences with certain properties. This is the best way to relate evidence and meaning, they thought, and what makes science special. This logical positivism approach to science became known as rational reconstruction.
An empirical interpretation of such language was need such as “There is an object X, and object has property P.” However science isn’t just about observations and properties, but also explanations. Observations and properties can be listed but we also need to elaborate a theory and make predictions, and this goes beyond experience. If science must limit itself to experience, how can it also go beyond experience?
How can we say object X is fragile, without violating empiricism and enter the realm of metaphysics? Fragile is not an observation term, but a disposition term. To solve this problem we have to place object X under certain test conditions. “X” must be defined as “fragile” in the condition that if we were to strike it, “X” would break”. This is a counter-factual conditional and cannot be defined in terms of the logical or observational vocabulary. Such conditionals depend on how the world would behave if it were different that it presently is, that is, under experimental conditions.
We need to move away from the observational level in order to explain phenomena and generate predictions about the world and increasingly complex phenomena. Thus, vocabulary must keep expanding, interpreting new terms and creating statements linking those terms.
The structure of a scientific theory is like a mathematical theory, and general laws serve as axioms, such as Newton’s laws. New statements provide empirical meaning that directly connect to observations. Sentences or statements that does not hook up to other statements are called isolated sentences, they don’t generate new predictions, and don’t add anything to the theory. Another way logical positivists avoid metaphysics is by refusing theories or statements about unobservable reality. A statement is only meaningful if it can be verified observationally.
For the logical positivists, the connections between theoretical terms are crucial for deriving observations, not for describing reality. Many statements in a scientific theory are inferences, saying that it is all right to infer this from that, that’s the instrumental conception of scientific theories. Science doesn’t make statements that go beyond observation, but make statements about patterns in experience.
A scientific theory is an axiomatic system, all relevant observation statements are deduced from a set of logically consistent, true theory statements, and upon rules for correlating theory and observation.
Logical Positivism persisted from the 1930s into the 1960s, offering hope that a classically objective theory of scientific knowledge was possible, but in the end, Logical Positivism failed. Carl Hempel criticized the separability of theory and observation statements, while W. V. O. Quine dismissed the distinctions between analytic and synthetic sentences, and between theory and observation statements. The verifiability criterion didn’t resist because some non-verifiable terms were inevitable in meaningful theories.
Methodological naturalism and assumptions
The methods of science are called methodological naturalism, in simple terms it underlines that material effects must have material causes. The material includes all macroscopic matter, all forms of energy, dark matter and dark energy recently discovered. This philosophy follows natural laws, therefore it cannot invoke miracles or supernatural phenomena as an explanation for any experiment or observation outcome. Science requires that we follow the methods that assume there is nothing beyond the natural world, and does not rely upon any supernaturalism.
For example, one scientific assumption is that of an objective reality. Without that assumption it’s impossible to investigate how the universe works. Another assumption is that the world is predictable and, therefore, knowable.
Falsifiability
Non-material causes cannot be falsified; therefore, they fail to meet a necessary criterion for science - they are not constrained in any way, they have no limits, they do not follow the laws of nature of material cause and effect. In short, constraint is necessary for falsifiability.
Bertrand Russell explored the relationship between testable and untestable claims and created an analogy known as Russell's teapot: a hypothetical teapot proposed that is orbiting the Sun between Earth and Mars to make the point that not all claims that cannot be proven false should be accepted as true.
Therefore, one cannot prove the teapot doesn’t exist, but is it reasonable to conclude that it does exist?
This example is used to make the point that the burden of proof for any scientific claim lies with those making the claim. The inability to prove something false is not sufficient justification for the claim.
This also raises the issue of proving a negative in science. While it’s not possible to absolutely prove the non-existence of something, given what we know about the laws of science and nature we might be able to demonstrate that something is impossible.
Pathways and methods of science
An observation leads to a hypothesis, and the hypothesis is then tested by an experiment. After the results, the hypothesis is refined. Then, the experimental phase is repeated.
Observation → hypothesis → experiment → refine → repeat
However, science can follow many pathways, for example, a hypothesis may come before a single observation.
Scientific methods develop a model of how the world works. Hypotheses and theories must explain nature and generate predictions about observations. Scientific theories are explanations for a pattern of observations, when there is more than one explanation that can account for data we already have, an experiment can be a way to separate them. A theory is only useful if it makes predictions that are different than other existing theories.
The highest form of criticism is: “not even wrong”. This is a common phrase used in scientific criticism, meaning that the theory does not make specific predictions and is therefore useless, an idea that can’t be tested or refuted isn’t even wrong.
All conclusions in science are provisional and subject to further evidence and theories. There’s no metaphysical certitude, scientists and critical thinkers need to be comfortable with uncertainty.
Occam’s razor
Another way to separate two competing theories that account for the same data, if there is no empirical method, is a philosophical rule of thumb called Occam’s razor, or the rule of parsimony. Occam’s razor states that the theory with the fewest new (ad hoc) assumptions is preferred to make the theory work, in principle that theory at least should be preferred until eliminated. Otherwise, we could endlessly generate ad-hoc theories to explain any given phenomenon.
Paradigm shifts
Science is a process of refinement. You can have paradigm shifts, resembling Kuhn’s paradigm shift model, but as science progresses, the process of refinement becomes more apparent.
For example, Einstein’s relativity did not prove Newton’s classical ideas wrong, it was a refinement to Newton and not a total replacement.
In many sciences, while refinements are possible, fundamental knowledge will not be overturned, because they are established to a high degree and have solid scientific knowledge.
For example, electrons do not spontaneously decide to change their fundamental properties.
Hard science should be considered so by its rigorous methods, objective outcomes and systematic observations, and not by the subject matter.
Empiricism
Berkeley, George. Three Dialogues between Hylas and Philonous. New York: Oxford University Press, 1998
Hume, David. A Treatise of Human Nature. New York: Oxford University Press, 2000.
Pragmatism
Dewey, John. Experience and Nature. New York: Dover Publications, 1958.
Logical positivism
Ayer, Alfred Jules. Language, Truth and Logic, 2nded. New York: Dover Publications, 1952.
Balashov, Yuri, and Alex Rosenberg, eds. Philosophy of Science: Contemporary Readings. pp. 132–140. New York: Routledge, 2002.
Boyd, Richard, Philip Gasper, and J. D. Trout, eds. The Philosophy of Science. Cambridge, pp. 71–84. pp. 299−315. MA: MIT Press, 1991 Science.
Science
Asimov, Isaac. Asimov’s New Guide to Science. London: Penguin Books, 1993. Asimov explains the process of science using classic historical examples, offering the human dimension of how science works.
Klemke, E. D., Robert Hollinger, and David Wyss Rudge ,eds. Introductory Readings in the Philosophy of Science. Amherst: Prometheus, 1988. The standard introductory text to the philosophy of science; better suited for a classroom than casual reading.
Hines, Terence. Pseudoscience and the Paranormal. Amherst: Prometheus Books, 2002. Hines takes a no-nonsense scientific look at paranormal belief in popular culture.
Blogs
Novella, Steven. “New Scientist on Miracles.”NeuroLogica Blog. http://theness.com/neurologicablog/index.php/new-scientist-on-miracles
Novella, Steven. “Science and Faith.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/science-and-faith
Novella, Steven. “The Context of Anecdotes and Anomalies.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/the-context-of-anecdotes-and-anomalies
For more information on Sérgio Fontinhas, see Big Fitness Project.
Tampilkan postingan dengan label Science. Tampilkan semua postingan
Tampilkan postingan dengan label Science. Tampilkan semua postingan
Guest post: Grand Conspiracy Thinking.
Conspiracy thinking is another type of pseudoscience permeated with cognitive biases and logical fallacies. It is also fuelled by errors in perception and memory. The entire process is epistemologically crippled, involving circular reasoning and avoiding any possibility of refutation.
This can be counter-productive when extreme. Grand conspiracies involve many people, multiple organizations or agencies, acting for long periods of time.
A conspiracy theory is defined as a proposed plot by powerful people or organizations working together in secret to accomplish some goal (1, 2, 3). Conspiracy theories are not by definition false; indeed, many real conspiracies have come to light over the years.
Grand conspiracy theories tend to divide the world into three groups:
1. The conspirators themselves, they are powerful, have incredible resources and evil intentions. However, they make silly simple mistakes, the conspiracy theorists notice.
2. The conspiracy theorists have the ability to read between the lines and see the invisible hand of the conspirators at work.
3. Everyone else in the world, the naive people, the ones who are not engaged in the conspiracy and don’t see that it exists.
The Paranoid Style in American Politics (1966) by Richard Hofstadter is considered to be the first scholarly essay on conspiracy thinking. The essay looks at conspiracy thinking as a psychopathology and paranoid delusional disorder. However, because it is so common, conspiracy thinking is not fully explained by this.
Conspiracy thinking is a way to make sense of complex or mysterious events, when people feel that they lack control or are being victimized. In fact, we often lack detailed information about important political events or other events, thus opening the door even further to conspiracy thinking to fill the gaps in our knowledge with ideas or notions that are comforting in some way.
Another reason people believe in conspiracy theories is cognitive dissonance. When confronted with contradictory evidence for their beliefs, people protect their beliefs to overcome the dissonance of being wrong.
Also in play is hindsight bias, in which we tailor after-the-fact explanations to what already happened.
Conspiracy theories are a form of pattern recognition, the cognitive form of pareidolia, a pattern imposed upon disconnected events or on random data, such as seeing an image in a stain, in a tree or a cloud. Conspiracy thinking is a hyperactive pattern detection.
Pattern recognition is also more common in response to feelings of powerlessness, pareidolia becomes more likely in research scenarios where subjects are made to feel more powerless. It can also channel feelings of anger.
Pattern recognition is filtered through the reality-testing module in our brains. An apparent pattern is tested and the brain decides whether it conforms to our internal model of reality.
In confirmation bias, there’s the tendency to see all evidence as confirmatory. Sometimes even negative evidence tends to reinforce their certainty and confidence. Being a self-reinforcing effect, it makes conspiracy theorists stubbornly resistant to change.
Another cognitive trap, this time the tendency to blame other people’s behavior on internal, rather than situational, factors.
Conspiracy theorists think that all actions and outcomes are deliberate and intended, there’s no coincidence or chance. They ignore that people may be innocently responding to a situation rather than deliberately orchestrating every detail.
Conspiracy thinking quickly becomes a closed-belief system, a belief system insulated from external refutation from facts and evidence. All evidence against the conspiracy can be explained as being part of the conspiracy itself. Any lack of evidence can be explained as having been removed or covered up by the conspirators. In this way, they can explain away any dis-confirming evidence and the lack of any evidence. In essence, they render themselves immune to any possible refutation.
They also render themselves immune to any burden of proof. They frequently shift the burden of proof onto others, when they have the burden of providing evidence in the first place.
Shifting the burden of proof is combined with moving goalpost. No matter how much evidence is provided, it is never enough. For them every quirky must be explained to an arbitrary level of detail.
Anomaly hunting is the process of looking for anything that seems out of the ordinary.
Complex historical events cannot be fully explained. This is because of the law of large numbers, the number of variables is so high that strange coincidences are will happen and are inevitable. There will always be anomalies to find, and conspiracy theorists use them to imply a dark conspiracy.
Conspiracy theorists often combine anomaly hunting with naive assumptions about how things should happen. Things are anomalous because they don’t fit with their idea of how events should have unfolded.
It is naive to assume that we can know with any detail what would result in any unprecedented event. Anything that does not fit their assumptions is an anomaly and evidence.
The conspiracy theorist proposes a false dichotomy - that either the standard explanation of events is true or their conspiracy is true.
If they can call it into doubt and poke holes in the standard story, through naive anomaly hunting, then their conspiracy must be true. All they have to do is find anomalies, cast doubt and replace the standard story of events, with no burden of proof upon themselves. There are other possibilities, so they are offering a false choice.
Widening the conspiracy is another tactic to render a conspiracy immune to contradictory evidence.
Grand conspiracies tend to grow, the power and resources attributed to the conspirators grow like there is almost nothing they cannot do.
Eventually, the theories will simply collapse under their own weight. It becomes too great to be plausible and maintain coordination and secrecy.
People who believe in one conspiracy theory tend to believe in many others. Psychologists at the University of Kent published a paper entitled “Dead and Alive: Beliefs in Contradictory Conspiracy Theories.”
“Conspiracy theories can form a monological belief system: A self-sustaining world-view comprised of a network of mutually supportive beliefs. The monological nature of conspiracy belief appears to be driven not by conspiracy theories directly supporting one another but by broader beliefs supporting conspiracy theories in general.” (4)
Belief in a particular theory is strongly predicted by belief in others, even if unrelated (2, 3, 5, 6).
Over time, the view of the world as a place ruled by conspiracies can lead to conspiracy becoming the default explanation for any given event - a unitary, closed-off world-view in which beliefs come together in a mutually-supportive network known as a monological belief system (3, 5, 6, 7).
The monological nature of conspiracism is driven not by conspiracy theories directly supporting one another but by the coherence of each theory with higher-order beliefs that support the idea of conspiracy in general. Conspiracism constitutes a monological belief system, drawing its coherence from central beliefs such as the conviction that authorities and officials engage in massive deception of the public to achieve their malevolent goals (4).
Another process may also be work, global coherence, which overrules contradictions, and “that everything happens for a reason….” (8).
The existence of absurd and implausible conspiracy theories can be used to dismiss any questioning of conventional explanations. Some even hypothesize that the government is responsible for some of the worst conspiracy theories in order to cover up the real one.
Conspiracy theories are often very simplistic or one dimensional explanations.
Conspiracism has become a major subcultural phenomenon.
Clarke found a trend of increasing vagueness in these modern conspiracist communities (9).
When a conspiracy is hypothesized it must be treated like any other scientific hypothesis, we cannot assume the conspiracy is true, look for confirming evidence, protect it from dis-confirming evidence, we cannot shift the burden of proof and support the theory with anomaly hunting. That’s the pseudoscience of grand conspiracy theories.
The principle of the null hypothesis in science: any theory or hypothesis is false until proven otherwise. Thus, the default rule of thumb with conspiracy theories is that they are false.
The more complex the conspiracy and the elements involved, the less likely it is to be true.
1. Coady, D. (2006). Conspiracy theories: The philosophical debate. Aldershot: Ashgate.
2. Douglas, K.M., & Sutton, R.M. (2008). The hidden impact of conspiracy theories: Perceived and actual influence of theories surrounding the death of Princess Diana. Journal of Social Psychology, 148,210–222.
3. Goertzel, T. (1994). Belief in conspiracy theories. Political Psychology, 15, 731–742.
4. Michael J. Wood, Karen M. Douglas, and Robbie M.Sutton. Dead and Alive: Beliefs in Contradictory conspiracy Theories. Social Psychological and Personality Science January 25, 2012
5. Swami, V.,Chamorro-Premuzic, T., & Furnham, A. (2010). Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs. Applied Cognitive Psychology, 24, 749–761.
6. Swami, V., Coles, R., Stieger, S., Pietschnig, J.,Furnham, A., Rehim, S., & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Evidence of a monological belief system and associations between individual psychological differences and real-world and fictitious conspiracy theories. British Journal of Psychology, 102, 443–463.
7. Clarke, S. (2002). Conspiracy theories and conspiracy theorizing. Philosophy of the Social Sciences, 32, 131–150.
8. Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences,12, 435–502.
9. Clarke, S. (2007). Conspiracy theories and the Internet: Controlled demolition and arrested development. Episteme: A Journal of Social Epistemology, 4, 167–180.
Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998):175–220.
Goertzel, Ted. “The Conspiracy Meme.” CSI. http://www.csicop.org/si/show/the_conspiracy_meme
Klass, Philip J. The Real Roswell Crashed-Saucer Coverup. Amherst: Prometheus Books, 1997.
Novella, Steven. “Conspiracy Thinking: Skepticism’s Evil Twin.”
NeuroLogica Blog. http://theness.com/neurologicablog/index.php/conspiracy-thinking-skepticisms-evil-twin
Hofstadter, Richard. The Paranoid Style in American Politics (1966).
Posner, Gerald. Case Closed. New York: Anchor Books, 1994. The definitive book on the John F. Kennedy assassination.
Goldwag, Arthur. Cults, Conspiracies, and Secret Societies. New York: Vintage Books,2009.
Vankin, J., and J. Whalen. The Fifty Greatest Conspiracies of All Time. New York: Citadel, 1995.
For more information on Sérgio Fontinhas, see Big Fitness Project.
This can be counter-productive when extreme. Grand conspiracies involve many people, multiple organizations or agencies, acting for long periods of time.
A conspiracy theory is defined as a proposed plot by powerful people or organizations working together in secret to accomplish some goal (1, 2, 3). Conspiracy theories are not by definition false; indeed, many real conspiracies have come to light over the years.
Grand conspiracy theories tend to divide the world into three groups:
1. The conspirators themselves, they are powerful, have incredible resources and evil intentions. However, they make silly simple mistakes, the conspiracy theorists notice.
2. The conspiracy theorists have the ability to read between the lines and see the invisible hand of the conspirators at work.
3. Everyone else in the world, the naive people, the ones who are not engaged in the conspiracy and don’t see that it exists.
The Paranoid Style in American Politics (1966) by Richard Hofstadter is considered to be the first scholarly essay on conspiracy thinking. The essay looks at conspiracy thinking as a psychopathology and paranoid delusional disorder. However, because it is so common, conspiracy thinking is not fully explained by this.
Conspiracy thinking is a way to make sense of complex or mysterious events, when people feel that they lack control or are being victimized. In fact, we often lack detailed information about important political events or other events, thus opening the door even further to conspiracy thinking to fill the gaps in our knowledge with ideas or notions that are comforting in some way.
Another reason people believe in conspiracy theories is cognitive dissonance. When confronted with contradictory evidence for their beliefs, people protect their beliefs to overcome the dissonance of being wrong.
Also in play is hindsight bias, in which we tailor after-the-fact explanations to what already happened.
1. Pattern Recognition
Conspiracy theories are a form of pattern recognition, the cognitive form of pareidolia, a pattern imposed upon disconnected events or on random data, such as seeing an image in a stain, in a tree or a cloud. Conspiracy thinking is a hyperactive pattern detection.
Pattern recognition is also more common in response to feelings of powerlessness, pareidolia becomes more likely in research scenarios where subjects are made to feel more powerless. It can also channel feelings of anger.
2. Reality testing and pattern recognition
Pattern recognition is filtered through the reality-testing module in our brains. An apparent pattern is tested and the brain decides whether it conforms to our internal model of reality.
3. Confirmation bias
In confirmation bias, there’s the tendency to see all evidence as confirmatory. Sometimes even negative evidence tends to reinforce their certainty and confidence. Being a self-reinforcing effect, it makes conspiracy theorists stubbornly resistant to change.
4. Fundamental attribution error
Another cognitive trap, this time the tendency to blame other people’s behavior on internal, rather than situational, factors.
Conspiracy theorists think that all actions and outcomes are deliberate and intended, there’s no coincidence or chance. They ignore that people may be innocently responding to a situation rather than deliberately orchestrating every detail.
5. Closed-belief system
Conspiracy thinking quickly becomes a closed-belief system, a belief system insulated from external refutation from facts and evidence. All evidence against the conspiracy can be explained as being part of the conspiracy itself. Any lack of evidence can be explained as having been removed or covered up by the conspirators. In this way, they can explain away any dis-confirming evidence and the lack of any evidence. In essence, they render themselves immune to any possible refutation.
6. Shifting the burden of proof
They also render themselves immune to any burden of proof. They frequently shift the burden of proof onto others, when they have the burden of providing evidence in the first place.
7. Moving the goalpost
Shifting the burden of proof is combined with moving goalpost. No matter how much evidence is provided, it is never enough. For them every quirky must be explained to an arbitrary level of detail.
8. Anomaly hunting
Anomaly hunting is the process of looking for anything that seems out of the ordinary.
Complex historical events cannot be fully explained. This is because of the law of large numbers, the number of variables is so high that strange coincidences are will happen and are inevitable. There will always be anomalies to find, and conspiracy theorists use them to imply a dark conspiracy.
9. Naive assumptions
Conspiracy theorists often combine anomaly hunting with naive assumptions about how things should happen. Things are anomalous because they don’t fit with their idea of how events should have unfolded.
It is naive to assume that we can know with any detail what would result in any unprecedented event. Anything that does not fit their assumptions is an anomaly and evidence.
10. False dichotomy
The conspiracy theorist proposes a false dichotomy - that either the standard explanation of events is true or their conspiracy is true.
If they can call it into doubt and poke holes in the standard story, through naive anomaly hunting, then their conspiracy must be true. All they have to do is find anomalies, cast doubt and replace the standard story of events, with no burden of proof upon themselves. There are other possibilities, so they are offering a false choice.
11. Widening the conspiracy
Widening the conspiracy is another tactic to render a conspiracy immune to contradictory evidence.
Grand conspiracies tend to grow, the power and resources attributed to the conspirators grow like there is almost nothing they cannot do.
Eventually, the theories will simply collapse under their own weight. It becomes too great to be plausible and maintain coordination and secrecy.
Monological belief system
People who believe in one conspiracy theory tend to believe in many others. Psychologists at the University of Kent published a paper entitled “Dead and Alive: Beliefs in Contradictory Conspiracy Theories.”
“Conspiracy theories can form a monological belief system: A self-sustaining world-view comprised of a network of mutually supportive beliefs. The monological nature of conspiracy belief appears to be driven not by conspiracy theories directly supporting one another but by broader beliefs supporting conspiracy theories in general.” (4)
Belief in a particular theory is strongly predicted by belief in others, even if unrelated (2, 3, 5, 6).
Over time, the view of the world as a place ruled by conspiracies can lead to conspiracy becoming the default explanation for any given event - a unitary, closed-off world-view in which beliefs come together in a mutually-supportive network known as a monological belief system (3, 5, 6, 7).
The monological nature of conspiracism is driven not by conspiracy theories directly supporting one another but by the coherence of each theory with higher-order beliefs that support the idea of conspiracy in general. Conspiracism constitutes a monological belief system, drawing its coherence from central beliefs such as the conviction that authorities and officials engage in massive deception of the public to achieve their malevolent goals (4).
Another process may also be work, global coherence, which overrules contradictions, and “that everything happens for a reason….” (8).
Considerations
The existence of absurd and implausible conspiracy theories can be used to dismiss any questioning of conventional explanations. Some even hypothesize that the government is responsible for some of the worst conspiracy theories in order to cover up the real one.
Conspiracy theories are often very simplistic or one dimensional explanations.
Conspiracism has become a major subcultural phenomenon.
Clarke found a trend of increasing vagueness in these modern conspiracist communities (9).
When a conspiracy is hypothesized it must be treated like any other scientific hypothesis, we cannot assume the conspiracy is true, look for confirming evidence, protect it from dis-confirming evidence, we cannot shift the burden of proof and support the theory with anomaly hunting. That’s the pseudoscience of grand conspiracy theories.
The principle of the null hypothesis in science: any theory or hypothesis is false until proven otherwise. Thus, the default rule of thumb with conspiracy theories is that they are false.
The more complex the conspiracy and the elements involved, the less likely it is to be true.
References
1. Coady, D. (2006). Conspiracy theories: The philosophical debate. Aldershot: Ashgate.
2. Douglas, K.M., & Sutton, R.M. (2008). The hidden impact of conspiracy theories: Perceived and actual influence of theories surrounding the death of Princess Diana. Journal of Social Psychology, 148,210–222.
3. Goertzel, T. (1994). Belief in conspiracy theories. Political Psychology, 15, 731–742.
4. Michael J. Wood, Karen M. Douglas, and Robbie M.Sutton. Dead and Alive: Beliefs in Contradictory conspiracy Theories. Social Psychological and Personality Science January 25, 2012
5. Swami, V.,Chamorro-Premuzic, T., & Furnham, A. (2010). Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs. Applied Cognitive Psychology, 24, 749–761.
6. Swami, V., Coles, R., Stieger, S., Pietschnig, J.,Furnham, A., Rehim, S., & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Evidence of a monological belief system and associations between individual psychological differences and real-world and fictitious conspiracy theories. British Journal of Psychology, 102, 443–463.
7. Clarke, S. (2002). Conspiracy theories and conspiracy theorizing. Philosophy of the Social Sciences, 32, 131–150.
8. Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences,12, 435–502.
9. Clarke, S. (2007). Conspiracy theories and the Internet: Controlled demolition and arrested development. Episteme: A Journal of Social Epistemology, 4, 167–180.
Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998):175–220.
Goertzel, Ted. “The Conspiracy Meme.” CSI. http://www.csicop.org/si/show/the_conspiracy_meme
Klass, Philip J. The Real Roswell Crashed-Saucer Coverup. Amherst: Prometheus Books, 1997.
Novella, Steven. “Conspiracy Thinking: Skepticism’s Evil Twin.”
NeuroLogica Blog. http://theness.com/neurologicablog/index.php/conspiracy-thinking-skepticisms-evil-twin
Hofstadter, Richard. The Paranoid Style in American Politics (1966).
Posner, Gerald. Case Closed. New York: Anchor Books, 1994. The definitive book on the John F. Kennedy assassination.
Goldwag, Arthur. Cults, Conspiracies, and Secret Societies. New York: Vintage Books,2009.
Vankin, J., and J. Whalen. The Fifty Greatest Conspiracies of All Time. New York: Citadel, 1995.
For more information on Sérgio Fontinhas, see Big Fitness Project.
Guest post: Understanding scientific research [2]: Types and quality of evidence, by Sérgio Fontinhas.
Understanding scientific research [2]: Types and quality of evidence
In conducting scientific research it’s imperative to know how to evaluate the scientific literature and recognize the different nature and strength of scientific evidence available. Evidence is important to figure out what is true, but not to defend what you wish to be true.
Scientific beliefs and conclusions must be based on solid evidence, but because there are several kinds of evidence it’s not an easy task and not obvious how one should interpret evidence. There are different kinds of evidence with different strengths and weaknesses. Methods of balancing and comparing the different kinds of evidence are required.
The first distinction of scientific evidence is between experimental and observational evidence. The first step is how to assess an individual study. This has already been explored on part 1.
Then, all available evidence has to be balanced and compared.
1. Experimental versus Observational Studies
Experimental studies control as many variables as possible to measure a specific outcome. The goal is to isolate one variable so that the specific effects are determined.
The strengths of experimental studies include:
1. Controlling and isolating variables.
2. Quantitative in measuring some specific feature or outcome.
3. Statistical because there are comparison groups.
Weaknesses are also present:
1. Artefacts.
2. Interfering with a system may change its behavior.
3. May not be representative of real-world experiences.
4. May not be practical. There are certain kinds of experimental studies simply cannot be performed, for example exposing a person or group to toxins and anything that puts their well-being of life at risk.
Observational studies ideally do not intervene, they observe the world with no specific intervention. These are useful for correlations, to correlate a risk factor to a known disease for example. Or for palaeontology and archaeology, discovering and examining fossils, for astronomy observing the light from stars.
Strengths of observational studies:
1. Large amounts of data by observing what already exists.
2. Group comparisons.
3. Minimal intervention in the natural behavior of the system.
Weaknesses:
1. Do not control many variables.
2. Always subject to unknown variables.
3. Demonstrate correlation but cannot establish definitively cause and effect.
This two types of evidence, experimental and observational, are complementary and work together to provide different kinds of information with different strengths and weaknesses.
2. Examining the Data
Studies with only a few subjects (observational or experimental) are likely to be erroneous, because of the greater noise-to-signal ratio. Random effects to average out with large studies and sample sizes.
The statistical significance of results is often expressed as a P-value: the probability to get the results given the null hypothesis, which is the hypothesis that the phenomenon studied does not exist as opposed to the evidence establishing that the phenomenon does exist.
A p-value=0.05 means that 1 in 20 studies, where the null hypothesis is in fact true, will still give a positive result - 1 false positive result.
Barely significant results are not as compelling as highly significant results, because we can expect from chance alone the literature to be full of studies that are false positive.
However, statistical significance is not everything. A systematic flaw or bias in a study design can systematically bias the results in one direction (1).
The effect size is also needed, and we need to consider also how large the effect is. The smaller the effect size, the greater the probability that some subtle bias influenced the outcome, therefore tiny effect sizes are always tricky, even effect sizes that are right at the limit of our ability to detect them.
We also need to consider the drop-out rate of a study. A drop-out rate between 10-20% seriously reduces the reliability of the outcome. All data must be counted to avoid creating false results if only a subset of the data is counted. Selecting data can systematically bias results and make the outcome misleading.
Every single study must be systematically reviewed and then published, this is another way of counting all the data of all of the studies.
Statisticians do what’s called funnel plots, the scatter of results from different studies on the same question.
The literature if full of preliminary studies, where there’s large bias toward being falsely positive. However, as more rigorous studies are conducted, they begin to show the real effect.
As better studies are conducted over time effect sizes tend to shrink – this is called the decline effect. Even when the effect is real, the effect size is usually small in the more rigorous studies. For non-existent phenomena, the effect size shrinks to zero. The less rigorous studies tend to be more variable in their results and more shifted or biased towards the positive, however that gets worked out as studies become more rigorous.
3. Prospective and retrospective
A prospective study observes the behavior and outcome, conversely a retrospective study looks back at events and outcomes that have already occurred.
In principle prospective studies are more rigorous, this is because there are fewer confounding factors, are more systematic, and the samples are more representative. However for retrospective studies, there’s the potential for multiple bias.
4. Blinded and double blinded
In any rigorous study where the outcome of the results is be blinded, scientists are blinded to what they’re looking for in the intervention or in the control group. This reduces the subconscious researcher bias in the results. Experimental studies generally should be blinded in order to be reliable. Observational studies, on the other hand, can only be partially blinded.
The bias of the researcher can influence the results, in other words the results tend to be what they expect. Erroneous results disappear when proper blinding is put into place.
5. Controls
The controls must also be adequate. For example, what is the subject of the study being compared to? In medical trials an active control may obscure the comparison to the treatment, or the placebo may cause a negative outcome, making the experimental treatment seem artificially better. Perhaps the standard treatment to which a new treatment is being compared may be ineffective, making the new treatment seem more effective than it really is.
The control group being studied must also be representative of the population of interest. For example the 1936 Literary Digest poll failed mostly due to unrepresentative sample size and failure “to include the supposed core of Roosevelt’s support, the poor… and were excluded from the pool” (1). However others also pointed out “ both the sample and the response rate as being flawed”, and asserted that “the initial bias towards over-representation of Republicans in the sample was exacerbated by the fact that better-educated and wealthy people who tended to be Landon supporters were more likely to respond to the survey.” (1).
6. Examining the literature
Individual studies can be preliminary and flawed, or they can be rigorous and methodologically sound, but either way a single study is a single study. Few studies are so large, rigorous, and unambiguous in outcome that they can stand alone and can be considered definitive studies. Individual studies need to be put into the context of the overall research, or the published literature.
We must evaluate if an individual study has been replicated by independent labs and researchers. If so, we must evaluate if the results are consistent or mixed. In addition, did the results look at the same thing and control for the same variables?
When looking at any research question all literature must be considered and put it into context.
7. Publication bias
Publication bias is the tendency for researchers to push their results to be published when the results are interesting, positive, and somehow good for their career and reputation. Journal editors can have a bias toward publishing positive studies, potentially creating good press releases and draw attention to their journal.
8. Meta-analysis
Meta-analysis looks at many different studies addressing similar questions, it’s a study of studies which combines the results of multiple studies into a new statistical analysis for a greater statistical power.
However, there’s still new possibilities for bias. If the preliminary studies were poorly designed and biased, the meta-analysis will still reflect the bias of those preliminary studies. In fact, a meta-analysis a poor predictor of the outcome of later large, definitive studies, only about 60-70% (2).
9. Systematic reviews
Systematic reviews also look at all the evidence and consider the quality of each study. They look for patterns in the literature, consistency, replication, and relation to effect size and study quality.
Like meta-analysis, systematic reviews are also subject to bias. We need to look at which studies are included; the inclusion criteria; the methods used to find studies. All of these can affect the outcome of the systematic review.
The take home message is, all this design in scientific studies and methods of evaluating the literature is a way of compensating for our biases, flaws, fallacies. Evidence should and must be used to figure out what is true, rather than to defend what we already wish to be true.
References
1. Squire, Peverill. “Why the 1936 Literary Digest Poll Failed. Public Opin Q (1988) 52 (1): 125-133.
2. LeLorier, Jacques, Geneviève Grégoire, Abdeltif Benhaddad, Julie Lapierre, and François Derderian. “Discrepancies between Meta-Analyses and Subsequent Large Randomized, Controlled Trials.” New England Journal of Medicine 337 (1997): 536–542.
Novella, Steven. “Evidence in Medicine: Correlation and Causation.” Science-Based Medicine. http://www.sciencebasedmedicine.org/index.php/evidence-in-medicine-correlation-and-causation
Novella, Steven. “Evidence in Medicine: Experimental Studies.” Science-Based Medicine. http://www.sciencebasedmedicine.org/index.php/evidence-in-medicine-experimental-studies
Taper, Mark L.,and Subhash R. Lele. The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations. Chicago: University of Chicago Press, 2004.
Guest post: Denialism as Pseudoscientific Thinking.
In pseudoscience there’s a subtype called Denialism. Denialism seeks to deny an established science and violate multiple principles of logic, and scientific methodology, this is mostly because of a priori beliefs and preconceptions. Typically the same cognitive and logical errors are committed in denialism reasoning.
The whole process starts with a desired conclusion, that a generally accepted scientific or historical claim is not true. Denialists have ideological reasons, and engage in motivated reasoning, rationalizing away the undesired claim.
In essence and practical terms, they work backward from their desired conclusion, filling in justifications.
In moving the goalposts, they always demand more evidence for a claim, even if currently available. However when that burden of evidence is met, the goalposts are moved and more evidence is demanded.
They may use vagueness in defining a certain term to move the goalpost away from any possible dis-confirming evidence.
Because science has gaps, they explore them as if it the specific scientific theory being discussed is invalid or not well established.
Let’s take the example of HIV denial. Deniers often demand a single study or scientific paper establishing HIV as the cause of AIDS. However, it is not established by a single study but rather by a large body of evidence.
In scientific reasoning we must see if the gaps are slowly being filled, and if predictions are met, and if it fits together with other lines of evidence, observational or experimental.
If a theory has been going around in circles and not progressing, that is a strong indication of pseudoscience.
Disagreements within a discipline are explored, often small details, as if the science in question is not solid.
Another strategy the narrowing of evidence that may count as “scientific”. The most common is using the logical fallacy of confusing correlation with causation.
Correlation is not the same as causation, not necessarily anyway. Correlations need to be used properly, and multiple correlations can triangulate a specific causal relationship observed in a correlation. Epidemiology is based on correlations and observational evidence, if they were invalid the entire field simply would vanish.
They can even deny all historical sciences such as astronomy, geology, or even forensics.
This is an argument from ignorance. If a version of events is not true then the alternate claim or version must be. However, they rarely provide positive evidence for their alternate claim.
Little factoids can be gathered and taken out of context. The goal is to sow doubt, uncertainty, and distrust, focusing on apparent inconsistencies, or gaps. However in healthy skepticism we consider all the evidence in the proper perspective, and even though knowledge is incomplete, reliable conclusions can be achieved.
As a last resort comes the conspiracy theory, claiming that the scientific evidence itself is fraudulent, a grand conspiracy. This tactic allows them to dismiss all the evidence and rationalize it away.
Grant, John. Denying Science. Amherst: Prometheus Books, 2011.
Novella, Steven. “More on God of the Gaps.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/more-on-god-of-the-gaps
Novella, Steven. “Skepticism and Denial.” The NESS. http://www.theness.com/index.php/skepticism-and-denial
Specter, Michael. Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives. London: Penguin Press, 2009
Tokuno, Hajime. “Holocaust Denial.” The NESS. http://www.theness.com/index.php/holocaust-denial
For more information on Sérgio Fontinhas, see Big Fitness Project.
The whole process starts with a desired conclusion, that a generally accepted scientific or historical claim is not true. Denialists have ideological reasons, and engage in motivated reasoning, rationalizing away the undesired claim.
In essence and practical terms, they work backward from their desired conclusion, filling in justifications.
1. Moving the goalposts
In moving the goalposts, they always demand more evidence for a claim, even if currently available. However when that burden of evidence is met, the goalposts are moved and more evidence is demanded.
They may use vagueness in defining a certain term to move the goalpost away from any possible dis-confirming evidence.
2. Unreasonable demand for evidence
Because science has gaps, they explore them as if it the specific scientific theory being discussed is invalid or not well established.
Let’s take the example of HIV denial. Deniers often demand a single study or scientific paper establishing HIV as the cause of AIDS. However, it is not established by a single study but rather by a large body of evidence.
In scientific reasoning we must see if the gaps are slowly being filled, and if predictions are met, and if it fits together with other lines of evidence, observational or experimental.
If a theory has been going around in circles and not progressing, that is a strong indication of pseudoscience.
3. Pointing out disagreements
Disagreements within a discipline are explored, often small details, as if the science in question is not solid.
4. Denying entire categories of evidence
Another strategy the narrowing of evidence that may count as “scientific”. The most common is using the logical fallacy of confusing correlation with causation.
Correlation is not the same as causation, not necessarily anyway. Correlations need to be used properly, and multiple correlations can triangulate a specific causal relationship observed in a correlation. Epidemiology is based on correlations and observational evidence, if they were invalid the entire field simply would vanish.
They can even deny all historical sciences such as astronomy, geology, or even forensics.
5. False dichotomy
This is an argument from ignorance. If a version of events is not true then the alternate claim or version must be. However, they rarely provide positive evidence for their alternate claim.
6. Campaign of Doubt
Little factoids can be gathered and taken out of context. The goal is to sow doubt, uncertainty, and distrust, focusing on apparent inconsistencies, or gaps. However in healthy skepticism we consider all the evidence in the proper perspective, and even though knowledge is incomplete, reliable conclusions can be achieved.
7. Conspiracy theory
As a last resort comes the conspiracy theory, claiming that the scientific evidence itself is fraudulent, a grand conspiracy. This tactic allows them to dismiss all the evidence and rationalize it away.
Grant, John. Denying Science. Amherst: Prometheus Books, 2011.
Novella, Steven. “More on God of the Gaps.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/more-on-god-of-the-gaps
Novella, Steven. “Skepticism and Denial.” The NESS. http://www.theness.com/index.php/skepticism-and-denial
Specter, Michael. Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives. London: Penguin Press, 2009
Tokuno, Hajime. “Holocaust Denial.” The NESS. http://www.theness.com/index.php/holocaust-denial
For more information on Sérgio Fontinhas, see Big Fitness Project.
Guest post: Science versus Pseudoscience.
Pseudoscience is so flawed that it cannot be considered legitimate science. Of course it is common to claim that one’s beliefs are scientific, but mostly they are not.
Pseudoscience lacks the true method of science and goes way beyond just a few errors, the methods themselves are so flawed that makes the theory suspicious.
Between the two extremes of science and pseudoscience there is a gray zone, but legitimate science and pseudosciences can still be identified. The denial of this two extremes in the continuum, is a false continuum logical fallacy, or philosophically called the demarcation problem.
The most prominent feature of this pathological science is working backward from desired results, or motivated reasoning. The result is that they make evidence fit into preconceived notions. They use biased logic and cherry-picked evidence in order to defend a desired conclusion. There’s no concern and effort to prove their own theories wrong.
This relates to the congruence bias, testing one’s own theory by looking for positive evidence and cherry-picked evidence.
They will only look for confirming evidence, avoid dis-confirming evidence, and may engage in special pleading and shifting the burden of proof.
In confirmation bias, they look for supportive evidence for their own desired conclusions, choosing only the evidence that supports their own theory, irrespective of quality, negative evidence.
Anecdotes are uncontrolled, or ad-hoc observations, and they are not systematic. They rely on confirmation bias and recall bias.
Low-grade evidence is often favored no matter how implausible it may be.
Emotional appeal is another typical tactic among pseudoscientists who try to defend their statements, claiming what people say is more important than actual numbers on paper.
Pseudoscientific belief may even be based upon a single case or observation, preliminary evidence, or even a single anecdote. This is the hasty generalization logical fallacy.
Pseudoscientific principles may also be based upon a philosophical idea, not been empirically tested or developed as a scientific theory.
This involves grandiose claims based upon preliminary evidence. Far-reaching claims overturn entire portions of well-established science, using very little research or tiny bits of evidence.
In extreme cases, pseudoscience leads to alternative science, all of science is replaced with an alternative version.
Pseudoscientists make bold claims that are often absolute and go way beyond the evidence. Pseudoscientists offer simple answers to complex questions, a theory of everything where one tiny casual source is used to explain the entire universe, if it comes to that.
Pseudoscientists generally cannot accept criticism and avoid the scientific community. They claim being victim of a conspiracy and stay away from mainstream science and community.
Pseudoscientists use vague terms and words to obfuscate, so they can shift the definition around, use it in different ways at different times when it suits them, to confuse others and avoid explaining their point. Vague terms such as “information” or “energy” are often used with no specificity as in a scientific discussion.
Pseudosciences fail to progress, and tend to be stagnant. They are ad nauseam trying to establish their theory rather than build a body of evidence for it.
Anomaly hunting is yet another common feature in which they search for anomalies trying to establish a conclusion, which does not seek to refute or explore other alternatives.
Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998): 175–220.
Novella, Steven. “Anomaly Hunting.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/anomaly-hunting
Pigliucci, Massimo. Nonsense on Stilts: How to Tell Science from Bunk. Chicago: University of Chicago Press, 2010
Shermer, Michael, The Borderlands of Science: Where Sense Meets Nonsense. New York: Oxford University Press, 2001.
Gardner, Martin. Fads and Fallacies in the Name of Science. Mineola: Dover Publications, 1957
Shermer, Michael. Why People Believe Weird Things. New York: Henry Holt/Times Books, 1997.
For more information on Sérgio Fontinhas, see Big Fitness Project.
Pseudoscience lacks the true method of science and goes way beyond just a few errors, the methods themselves are so flawed that makes the theory suspicious.
Between the two extremes of science and pseudoscience there is a gray zone, but legitimate science and pseudosciences can still be identified. The denial of this two extremes in the continuum, is a false continuum logical fallacy, or philosophically called the demarcation problem.
Features of Pseudoscience
1. Motivated reasoning
The most prominent feature of this pathological science is working backward from desired results, or motivated reasoning. The result is that they make evidence fit into preconceived notions. They use biased logic and cherry-picked evidence in order to defend a desired conclusion. There’s no concern and effort to prove their own theories wrong.
This relates to the congruence bias, testing one’s own theory by looking for positive evidence and cherry-picked evidence.
2. Burden of proof and confirmation bias
They will only look for confirming evidence, avoid dis-confirming evidence, and may engage in special pleading and shifting the burden of proof.
In confirmation bias, they look for supportive evidence for their own desired conclusions, choosing only the evidence that supports their own theory, irrespective of quality, negative evidence.
3. Anecdotal evidence
Anecdotes are uncontrolled, or ad-hoc observations, and they are not systematic. They rely on confirmation bias and recall bias.
Low-grade evidence is often favored no matter how implausible it may be.
Emotional appeal is another typical tactic among pseudoscientists who try to defend their statements, claiming what people say is more important than actual numbers on paper.
Pseudoscientific belief may even be based upon a single case or observation, preliminary evidence, or even a single anecdote. This is the hasty generalization logical fallacy.
Pseudoscientific principles may also be based upon a philosophical idea, not been empirically tested or developed as a scientific theory.
4. Grandiose claims (Galileo syndrome)
This involves grandiose claims based upon preliminary evidence. Far-reaching claims overturn entire portions of well-established science, using very little research or tiny bits of evidence.
5. Alternative science
In extreme cases, pseudoscience leads to alternative science, all of science is replaced with an alternative version.
6. Absolute claims
Pseudoscientists make bold claims that are often absolute and go way beyond the evidence. Pseudoscientists offer simple answers to complex questions, a theory of everything where one tiny casual source is used to explain the entire universe, if it comes to that.
7. Hostility
Pseudoscientists generally cannot accept criticism and avoid the scientific community. They claim being victim of a conspiracy and stay away from mainstream science and community.
8. Vagueness
Pseudoscientists use vague terms and words to obfuscate, so they can shift the definition around, use it in different ways at different times when it suits them, to confuse others and avoid explaining their point. Vague terms such as “information” or “energy” are often used with no specificity as in a scientific discussion.
9. Stagnation
Pseudosciences fail to progress, and tend to be stagnant. They are ad nauseam trying to establish their theory rather than build a body of evidence for it.
10. Anomaly hunting
Anomaly hunting is yet another common feature in which they search for anomalies trying to establish a conclusion, which does not seek to refute or explore other alternatives.
Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998): 175–220.
Novella, Steven. “Anomaly Hunting.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/anomaly-hunting
Pigliucci, Massimo. Nonsense on Stilts: How to Tell Science from Bunk. Chicago: University of Chicago Press, 2010
Shermer, Michael, The Borderlands of Science: Where Sense Meets Nonsense. New York: Oxford University Press, 2001.
Gardner, Martin. Fads and Fallacies in the Name of Science. Mineola: Dover Publications, 1957
Shermer, Michael. Why People Believe Weird Things. New York: Henry Holt/Times Books, 1997.
For more information on Sérgio Fontinhas, see Big Fitness Project.