A false cause fallacy occurs when an argument assumes a causal relationship without sufficient evidence. The term represents a category of errors related to unmerited assumptions about cause and effect.
False cause fallacies can lead to misguided beliefs, decisions, and actions, so it’s important to know how to identify and analyze fallacies of causation.
What is a false cause fallacy?
The false cause fallacy is a general term for the error of attributing causality without adequate supporting evidence.
False cause fallacies are also known by the names questionable cause, faulty causality, and non causa pro causa (a Latin phrase that means “non-cause for cause”).
As a type of informal logical fallacy, false cause fallacies are said to render an argument unsound and represent content-level errors rather than structural errors.
The false cause fallacy is an umbrella concept, representing all the fallacies whose main error relates to attributing causality. When spoken of as a category, they are often called causal fallacies, in contrast to other general categories such as fallacies of relevance and fallacies of ambiguity.
What are different types of false cause fallacies?
False cause fallacies can be divided into several specific types of errors. They all involve faulty reasoning about cause-and-effect relationships between events or phenomena.
Post hoc ergo propter hoc fallacy
The post hoc ergo propter hoc fallacy, derived from the Latin phrase “after this, therefore because of this,” involves attributing causation based solely on the order of events.
Post hoc fallacies specifically posit that an earlier event must be the cause of a subsequent event.
Cum hoc ergo propter hoc
The cum hoc ergo propter hoc fallacy, based on the Latin for “with this, therefore because of this,” is the error of assuming that two events or phenomena that occur at the same time must have a cause-and-effect relationship.
Like the post hoc fallacy, the cum hoc fallacy is focused on timing. However, post hoc fallacies involve events that occur one after another, whereas cum hoc fallacies relate to concurrent events.
Correlation–causation fallacies involve assuming that there is a cause-and-effect relationship between variables based solely on a correlation in the data.
The correlation–causation fallacy encompasses the post hoc and cum hoc fallacies, but it also includes fallacies that aren’t focused on the timing of events.
An argument that commits the oversimplification fallacy (or fallacy of the single cause) makes an unmerited claim that an event or phenomenon has just one specific cause, overlooking the complexity of the issue.
Examples of the oversimplification fallacy can often be seen in discussions of health and disease. Illnesses that may have many contributing causes are often mistakenly attributed to a single factor, which may or may not be one of the disease’s actual causes.
False cause fallacy examples
Examples of the false cause fallacy can be found in many contexts, including media reports, advertising, political arguments, social media debates, and faulty interpretations of scientific research.
Frequently asked questions about false cause fallacy
How can I identify a false cause fallacy in an argument?
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.
What’s the difference between correlation and causation?
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.
What are some examples of false cause 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.