GRE Argument Essay Logical Fallacies List
Core GRE Argument Essay Logical Fallacies
The gre argument essay logical fallacies list includes faulty causality, sampling errors, false analogies, and non-sequitur conclusions. To score highly, students must identify these gaps between the evidence provided and the conclusion reached. Most GRE prompts rely on unwarranted assumptions that fail to account for alternative explanations or changing conditions over time.
Common Fallacies and Their Flaws
| Fallacy Type | Description | What the Author Assumes |
|---|---|---|
| Causal Flaw | Correlation is mistaken for causation. | That event A caused event B simply because A happened first. |
| Sampling Error | Conclusions are based on a small or biased group. | That a tiny subset represents the entire population accurately. |
| False Analogy | Comparing two things that are not truly alike. | That what worked in City A will automatically work in City B. |
| Hasty Generalization | Drawing broad conclusions from insufficient data. | That a single instance or data point proves a universal rule. |
| All Things Being Equal | Assuming conditions remain stable over time. | That data from five years ago is still valid and applicable today. |
Analyzing Causal and Temporal Flaws
Causal flaws are the backbone of most GRE Argument prompts. The author often cites a 'recent study' or a 'change in policy' followed by a specific outcome, implying the first caused the second. To debunk this, you must suggest alternative causes. For example, if a store's profits rose after hiring a new manager, the author ignores the possibility of a booming economy, a competitor closing down, or seasonal trends.
Sampling and Survey Errors
When a prompt mentions a survey, it is almost always flawed. Look for these specific issues:
- Self-selection bias: Only people with strong opinions responded to the survey.
- Small sample size: The survey only interviewed a dozen people out of thousands.
- Vague statistics: Terms like 'most' or 'many' are used instead of specific percentages or raw numbers.
- Leading questions: The survey questions may have been designed to elicit a specific answer.
Example: Deconstructing a Prompt
**Prompt Segment:** 'Last year, the city of Blueville saw a 20% decrease in crime after installing new streetlights. Therefore, the city of Redville should install the same lights to reduce its crime rate.' **Logical Flaws to Attack:** 1. **False Analogy**: Blueville and Redville may have different demographics, geography, or existing crime types. 2. **Causal Flaw**: The crime drop in Blueville might have been due to increased police patrols or a decrease in unemployment, not the lights. 3. **All Things Being Equal**: The 'last year' data might have been an anomaly and may not repeat in the future.
Practical Advice for the 6.0 Score
Use 'maybe' or 'possibly' when suggesting alternative explanations. Instead of saying the author is wrong, state that the argument 'fails to consider' or 'depends on the unwarranted assumption that' specific factors are identical. This maintains the analytical tone required by GRE graders.
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