The term “cost-benefit fallacy” is not a formally recognized logical fallacy, but it might be used to refer to errors in cost-benefit analysis.
Cost-benefit analysis is a framework for systematically evaluating the advantages and disadvantages of investments, policies, and other decisions in fields such as economics, public policy, and healthcare.
Mistakes in cost-benefit analyses can include the following:
- Confirmation bias: favoring information that supports preconceived notions
- Incomplete data: overlooking relevant costs or benefits
- Subjectivity: biases in valuing intangible benefits or costs
- Discounting: incorrect application of discount rates affecting future values
Time horizon: misjudging the appropriate timeframe for analysis
Continue reading: What is a cost-benefit fallacy?
To avoid being influenced by the base rate fallacy, consider the following strategies:
- Prioritize statistical data: Always consider the general frequency of an event before focusing on specific instances or results.
- Avoid overreliance on specifics: Don’t let compelling details overshadow overall statistical probabilities.
Apply Bayesian reasoning: Start with initial probabilities and systematically update them with new evidence to balance general data with specific information.
Continue reading: How can you avoid the base rate fallacy?
The following fictional scenario is an example of the base rate fallacy:
A search for extraterrestrial intelligence (SETI) program develops an algorithm with 99% accuracy for identifying alien signals among cosmic noise, where the actual occurrence of alien signals is estimated to be only 1 in a million. When the algorithm flags a signal as alien, the media reports that alien life has been contacted. This assumption is based on the algorithm’s high accuracy rate, but it ignores the extremely low probability that the signal is from alien life.
In this example, the media commits the base rate fallacy by ignoring statistical reality and focusing on a specific incident. Given the base rate of 1 alien signal in a million, the vast majority of flagged signals are false positives.
Continue reading: What is an example of the base rate fallacy?
False cause fallacies assume a causal relationship between events, as demonstrated in the following examples:
- A manager attributes a company’s profit increase to a new marketing campaign while ignoring market trends.
- The principal of a high school credits a new textbook for improved student grades while disregarding the impact of a new tutoring program.
- A city’s mayor takes credit for a reduction in crime, attributing it to increased policing, while overlooking the benefits of new community initiatives aimed at alleviating poverty and improving education.
There are several types of false cause fallacies that have specific names, including the post hoc fallacy and the cum hoc fallacy.
Continue reading: What are some examples of false cause fallacy?
In the correlation–causation fallacy, a perceived similarity or relationship between two variables is wrongly assumed to imply a cause-and-effect relationship. It’s important to understand the differences between correlation and causation:
- Correlation: variables change together or share common characteristics
- Causation: one variable, event, or phenomenon directly leads to another
The maxim “correlation does not imply causation” is often used to rebut the correlation–causation fallacy. Observing a similarity or relationship between two variables does not necessarily indicate a causal link.
Continue reading: What’s the difference between correlation and causation?
To identify a false cause fallacy, look for the following mistakes in an argument:
- Unsubstantiated causal claim: Assess whether the argument asserts a cause-and-effect relationship without providing adequate evidence to support the claim.
- Ignoring other possible causes: Observe whether the argument overlooks or dismisses other plausible explanations for the observed outcome.
- Correlation or timing assumed to prove causality: Beware of conclusions based solely on correlations or the order of events, which aren’t sufficient to prove causation.
Continue reading: How can I identify a false cause fallacy in an argument?
Cognitive biases and logical fallacies are distinct but related concepts that both involve errors in reasoning.
- Cognitive biases refer to inherent human tendencies toward specific erroneous thought patterns.
- Logical fallacies are errors in persuasive communication that undermine the validity or soundness of an argument.
Logical fallacies sometimes result from, or appeal to, cognitive biases.
Continue reading: What is the difference between cognitive biases and logical fallacies?
The fallacy of composition can be considered a type of hasty generalization fallacy.
Continue reading: What is the difference between the fallacy of composition and hasty generalization fallacy?
The direct opposite of the fallacy of composition is the fallacy of division.
- The fallacy of composition occurs when an argument incorrectly assumes that what is true for the parts is also true for the whole (e.g., if a company has some successful products, the company must be successful on the whole).
- The fallacy of division occurs when an argument incorrectly assumes that what is true for the whole is also true for its individual parts (e.g., if a car is well made overall, its individual components must be well made).
A related concept is the ecological fallacy, an error in statistical analysis where conclusions about individuals are wrongly inferred from group-level data. While not the exact opposite of the fallacy of composition, the ecological fallacy also involves the unwarranted transfer of qualities between parts and wholes
Continue reading: What is the opposite of the fallacy of composition?
Examples of non sequitur fallacies, also known as formal fallacies, aren’t easy to find in daily life because they typically occur in formal disciplines such as logic, mathematics, and physics. The following example illustrates the nature of a non sequitur fallacy:
- Premise: All cats are mammals.
- Premise: A dog is a mammal.
- Conclusion: Therefore, a dog is a cat.
More specifically, this example falls into the subcategory of the fallacy of the undistributed middle, in which the middle term in the premises doesn’t cover all possible cases, leading to a faulty conclusion.
Continue reading: What is an example of non sequitur?
