Causation vs correlation in LR
54 flashcards covering Causation vs correlation in LR for the LSAT Logical Reasoning section.
Causation and correlation are fundamental concepts in logical reasoning that help us understand relationships between events. Correlation occurs when two things happen together, like an increase in ice cream sales and drowning incidents during summer, but this doesn't mean one causes the other—it could be due to a third factor, like hot weather. Causation, on the other hand, means one event directly leads to another, such as smoking causing lung cancer. Mastering this distinction is crucial because mistaking correlation for causation can lead to flawed arguments and poor decision-making, which is why it's essential for logical analysis.
On the LSAT, causation versus correlation frequently appears in Logical Reasoning questions, especially in flaw identification, assumption, or strengthening/weakening argument types. Common traps include arguments that leap from observed patterns to unwarranted causal claims, ignoring alternative explanations or confounding variables. Focus on scrutinizing the evidence for direct links and considering counterexamples to build stronger evaluations. Always question whether a correlation truly implies causation.
Terms (54)
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Causation
Causation occurs when one event directly causes another to happen, meaning the first event is a necessary or sufficient factor in producing the second.
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Correlation
Correlation is a statistical relationship where two variables tend to occur together, but one does not necessarily cause the other.
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Difference between causation and correlation
The key difference is that causation implies one event directly produces another, while correlation only indicates they vary together without establishing a direct link.
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Post hoc fallacy
The post hoc fallacy assumes that because one event follows another, the first must have caused the second, which is a common error in causal reasoning.
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Correlation implies causation fallacy
This fallacy wrongly concludes that a correlation between two variables means one causes the other, ignoring possible alternative explanations.
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Causal indicator words
Words like 'causes,' 'leads to,' or 'results in' often signal a claim of causation in an argument, but they must be evaluated for evidence.
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Correlational indicator words
Terms such as 'associated with,' 'linked to,' or 'correlates with' suggest a relationship between variables without implying causation.
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Confounding variable
A confounding variable is an external factor that influences both the supposed cause and effect, potentially creating a false appearance of causation.
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Reverse causation
Reverse causation occurs when the presumed effect actually causes the presumed cause, flipping the direction of the relationship.
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Third variable problem
The third variable problem arises when a hidden factor causes both variables in a correlation, making it seem like one causes the other.
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Necessary condition for causation
A necessary condition for causation means the cause must be present for the effect to occur, though it alone may not be sufficient.
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Sufficient condition for causation
A sufficient condition for causation means the cause alone guarantees the effect, even if other factors are not present.
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Strengthening a causal argument
Strengthening a causal argument involves providing evidence like controlled experiments or ruling out alternative explanations to support the claimed cause-effect link.
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Weakening a causal argument
Weakening a causal argument can be done by introducing alternative causes, showing coincidences, or highlighting confounding variables that undermine the link.
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Alternative explanations
Alternative explanations are other possible causes for an observed effect that must be considered to avoid mistakenly attributing causation.
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Control groups in causal studies
Control groups are used in experiments to isolate the effect of the supposed cause by comparing outcomes with and without the variable.
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Random assignment in experiments
Random assignment ensures participants are evenly distributed in groups to minimize bias and strengthen claims of causation.
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Observational studies vs. experiments
Observational studies identify correlations but cannot prove causation due to lack of control, while experiments can establish causation through manipulation.
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Spurious correlation
A spurious correlation is an apparent relationship between variables that is actually due to coincidence or a third factor, not true causation.
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Coincidence vs. causation
Coincidence refers to events happening together by chance, whereas causation requires a direct mechanism linking them.
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Temporal precedence in causation
Temporal precedence means the cause must occur before the effect, a basic requirement for establishing a causal relationship.
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Common cause fallacy
The common cause fallacy assumes a direct causal link between two events when both are actually caused by a single underlying factor.
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Identifying causal claims
Identifying causal claims involves looking for language that asserts one event produces another and checking if evidence supports it.
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Evaluating evidence for causation
Evaluating evidence for causation requires assessing whether the evidence rules out correlations, coincidences, or confounding variables.
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Counterfactual reasoning
Counterfactual reasoning imagines what would happen if the supposed cause were absent, helping to test whether true causation exists.
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Flaw in assuming causation from correlation
A common flaw is assuming causation solely from a correlation, without considering if the relationship is coincidental or influenced by other factors.
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Causal chains
Causal chains are sequences where one event causes another, which in turn causes a third, and arguments may require tracing these links.
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Intervening variables
Intervening variables are factors that occur between a cause and effect, potentially altering or explaining the relationship.
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Moderating variables
Moderating variables influence the strength or direction of a causal relationship, depending on their presence or level.
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Self-selection bias
Self-selection bias occurs when participants choose their groups, potentially skewing results and weakening causal inferences.
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Biased samples
Biased samples are unrepresentative groups that can lead to incorrect causal conclusions by not accounting for population variations.
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Sample size issues
Sample size issues arise when studies are too small, making it hard to distinguish true causation from random correlations.
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Placebo effects
Placebo effects are perceived improvements due to expectation, not the treatment, which can mimic causation in medical studies.
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Double-blind experiments
Double-blind experiments keep both participants and researchers unaware of group assignments to prevent bias in establishing causation.
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Longitudinal studies
Longitudinal studies track variables over time, providing stronger evidence for causation by observing sequences of events.
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Cross-sectional studies limitations
Cross-sectional studies capture data at one point, limiting their ability to establish causation due to the lack of temporal data.
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Feedback loops
Feedback loops occur when an effect influences the original cause, complicating the identification of straightforward causation.
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Probabilistic causation
Probabilistic causation means a cause increases the likelihood of an effect but does not guarantee it, common in real-world arguments.
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Falsifiability of causal claims
Falsifiability means a causal claim can be tested and potentially disproven, a key aspect for evaluating its validity.
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Strategy for flaw questions on causation
For flaw questions, identify if the argument assumes causation from correlation and check for unaddressed alternatives or confounds.
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Answering strengthen questions
To answer strengthen questions, look for evidence that directly supports the causal link or eliminates rival explanations.
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Weaken questions involving correlation
For weaken questions, introduce possibilities that the observed correlation is due to chance, bias, or a third factor.
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Must be true based on causation
In must-be-true questions, ensure inferences follow logically from causal premises without assuming unstated correlations.
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Cannot be true with causation
Cannot-be-true questions may involve scenarios that contradict established causal relationships in the argument.
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Paradox in causal arguments
A paradox in causal arguments occurs when seemingly logical cause-effect chains lead to contradictory outcomes, requiring resolution.
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Principle questions on causation
Principle questions test if a general rule about causation applies to a specific scenario, like requiring temporal precedence.
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Analogy in causal arguments
Analogies in causal arguments compare situations to illustrate causation, but flaws arise if the comparisons are not apt.
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Counterexample to causal claims
A counterexample disproves a causal claim by showing an instance where the supposed cause does not produce the effect.
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Statistical significance in arguments
Statistical significance indicates that a correlation is unlikely due to chance, but it does not prove causation without further evidence.
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Randomized controlled trials
Randomized controlled trials are experiments that randomly assign variables to establish causation by minimizing biases.
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Causal inference from patterns
Causal inference from patterns involves deducing likely causes from repeated observations, but it requires caution against false positives.
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Overgeneralization in causation
Overgeneralization in causation occurs when an argument applies a causal relationship from a specific case to all similar cases without justification.
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Underestimating coincidences
Underestimating coincidences leads to mistakenly identifying them as causation, a trap in evaluating correlational data.
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Causation in policy arguments
In policy arguments, causation links proposed actions to outcomes, and flaws often involve ignoring unintended consequences.