This list may help us examine our own biases and beliefs. I reckon each of us has employed one or more of these biases, whether we realized it or not…. Curry invites contributions to the list….
From a post by Judith Curry on her blog, Climate Etc., October 4, 2020.
“The first principle is that you must not fool yourself, and you are the easiest person too fool.” – physicist Richard Feynman
Cognitive biases relate to self-deception that leads to incorrect conclusions based on cognitive factors, including information-processing shortcuts (heuristics) (Tversky and Kahnemann 1974). Cognitive biases can abound when reasoning and making judgments about a complex problem such as climate change.
Cognitive biases affecting belief formation that are of particular relevance to the science of climate change include:
- Confirmation bias: the tendency to search for or interpret information in a way that confirms one’s preconceptions
- Anchoring bias: the tendency to rely too heavily on one trait or piece of information, such as the mean or previous results.
- Framing bias: using an approach that is too narrow that pre-ordains the conclusion
- Overconfidence effect: unjustified, excessive belief
- Illusory correlations: false identification of relationships with rare or novel occurrences
- Ambiguity effect: the tendency to avoid options for which the probability of a favorable outcome is unknown
- Self-serving bias: a tendency for people to evaluate information in a way that is beneficial to their interests
- Belief bias: evaluating the logical strength of an argument based on belief in the truth or falsity of the conclusion
- Availability heuristic: The tendency to overestimate the likelihood of events with greater ‘availability’ in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be
A fallacy is logically incorrect reasoning that undermines the logical validity of the argument and leads to its assessment as unsound. There are many different classifications of fallacies. Below are some fallacies that I’ve seen used in arguments about climate science:
- Begging the question is a fallacy occurring in deductive reasoning in which the proposition to be proved is assumed implicitly or explicitly in one of the premises.
- Correlation implies causation is a logical fallacy by which two events that occur together are claimed to be cause and effect.
- Fallacy of distribution occurs when an argument assumes that what is true of the members is true of the class (composition), or what is true of the class is true of its members (division).
- Hasty generalization is the logical fallacy of reaching an inductive generalization based on too little evidence.
- Statistical special pleading occurs when the interpretation of the relevant statistic is ‘massaged’ by looking for ways to reclassify or requantify data from one portion of results, but not applying the same scrutiny to other categories.
- Fallacy of the single cause occurs when it is assumed that there is one simple cause of an outcome when in reality it may have been caused by a number of only jointly sufficient causes.
The category of intentional fallacies is not about how we fool ourselves, but how we try to fool others. Examples of intentional fallacies used routinely in the public debate on climate change include:
- Diverting the argument to unrelated issues with a red herring(ignoratio elenchi)
- Ad hominem fallacy: asserting that an argument is wrong because of something discreditable/not authoritative about the person making the argument.
- Appeal to motive: challenging a thesis by calling into question the motives of its proposer.
- Asserting that everyone agrees (argumentum ad populum, bandwagoning)
- Creating a ‘false dilemma’ (either-or fallacy) in which the situation is oversimplified
- Selectively using facts (card stacking)
- Making false or misleading comparisons (false equivalence and false analogy)
- Appeal to consequences of belief (argumentum ad consequentiam): an appeal to emotion that concludes a hypothesis or belief to be either true or false based on whether the premise leads to desirable or undesirable consequences.