Causation vs correlation
55 flashcards covering Causation vs correlation for the GMAT Verbal section.
Causation and correlation are fundamental concepts in understanding relationships between variables. Correlation simply means that two things tend to happen together, like higher temperatures and increased ice cream sales, but it doesn't imply that one causes the other—there might just be a coincidental link. Causation, however, means that one event directly leads to another, such as smoking causing lung cancer, which requires strong evidence to prove the connection rather than mere association.
On the GMAT Verbal section, especially in Critical Reasoning questions, this topic appears when evaluating arguments that often confuse correlation with causation. Common traps include assuming a causal relationship based on patterns alone, ignoring alternative explanations, or falling for flawed logic in passages. Focus on identifying these errors by scrutinizing evidence and questioning whether the argument truly demonstrates cause and effect.
Always check for possible confounding variables before accepting causation.
Terms (55)
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Correlation
Correlation is a statistical measure that describes the extent to which two variables fluctuate together, indicating an association without proving that one causes the other.
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Causation
Causation occurs when one event directly leads to another, meaning the first event is the reason the second happens, as opposed to mere coincidence or association.
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Correlation vs. Causation
Correlation vs. causation distinguishes between a mere association between variables and a situation where one variable directly influences the other, a common point of confusion in arguments.
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Positive Correlation
Positive correlation means that as one variable increases, the other also tends to increase, but this does not mean one causes the other without further evidence.
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Negative Correlation
Negative correlation indicates that as one variable increases, the other tends to decrease, yet this still does not imply causation without additional proof.
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Spurious Correlation
Spurious correlation happens when two variables appear related due to a third, unseen factor influencing both, misleadingly suggesting a direct link.
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Third Variable Problem
The third variable problem arises when an external factor affects two correlated variables, making it seem like they cause each other when they do not.
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Reverse Causation
Reverse causation is the error of assuming variable A causes B when in fact B causes A, often overlooked in arguments that confuse direction of influence.
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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 flaw in causal reasoning.
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Confounding Variable
A confounding variable is an extraneous factor that influences both the independent and dependent variables, potentially distorting the perceived relationship between them.
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Causal Claim
A causal claim asserts that one event directly produces another, requiring strong evidence like controlled experiments to be valid, unlike mere correlations.
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Correlational Evidence
Correlational evidence shows patterns between variables but cannot alone establish causation, as it might be due to chance or other factors.
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Causal Inference
Causal inference is the process of determining whether one variable truly causes another, often involving ruling out alternative explanations in arguments.
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Strengthening a Causal Argument
Strengthening a causal argument involves providing evidence that eliminates alternative explanations, such as showing no third variables are at play.
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Weakening a Causal Argument
Weakening a causal argument means introducing evidence of alternative causes, correlations without causation, or flaws like reverse causation.
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Controlled Experiment
A controlled experiment tests causation by manipulating one variable while keeping others constant, providing stronger evidence than observational data.
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Observational Study
An observational study notes associations between variables without intervention, often leading to correlations that may not indicate causation.
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Random Assignment
Random assignment in experiments ensures participants are evenly distributed, helping to isolate causation by minimizing the impact of confounding variables.
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Control Group
A control group in an experiment does not receive the treatment, allowing comparison to determine if the treatment caused the observed effects.
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Experimental Group
The experimental group receives the treatment in a study, and differences from the control group can suggest causation if other factors are controlled.
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Causation in GMAT Arguments
In GMAT arguments, causation is often inferred from patterns, but questions test whether such inferences hold up against potential correlations or fallacies.
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Correlation Coefficient
The correlation coefficient quantifies the strength and direction of a relationship between variables, ranging from -1 to 1, but does not prove causation.
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Common Trap: Assuming Causation
A common trap is assuming causation from correlation, which GMAT questions exploit by presenting data that looks causal but isn't.
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Bidirectional Causation
Bidirectional causation occurs when two variables influence each other mutually, complicating efforts to determine a clear cause-effect direction.
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No Correlation
No correlation means two variables show no consistent relationship, indicating that changes in one do not predict changes in the other.
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Causal Chain
A causal chain is a sequence of events where each one causes the next, which must be distinguished from coincidental series in arguments.
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Indirect Causation
Indirect causation happens when one variable affects another through an intermediary, requiring careful tracing to avoid misinterpretation.
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Direct Causation
Direct causation is when one variable immediately causes another without intermediaries, a concept tested when evaluating argument strength.
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Statistical Significance
Statistical significance indicates that a correlation is unlikely due to chance, but it still does not confirm causation without further analysis.
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Lurking Variable
A lurking variable is a hidden factor that affects the observed correlation, potentially explaining why it does not represent true causation.
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Causation Fallacy Example
In arguments, a causation fallacy might claim that ice cream sales cause drowning incidents due to their correlation, ignoring the third variable of summer heat.
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Correlation Without Causation Example
An example of correlation without causation is the link between shoe size and reading ability in children, both increasing with age but not causing each other.
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Strategy for Causal Questions
For causal questions on the GMAT, strategy involves identifying whether evidence supports a direct link or if alternatives like coincidence weaken the claim.
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Evaluating Causal Language
Evaluating causal language in passages means scrutinizing words like 'because' or 'leads to' to see if they are backed by evidence or merely assumed.
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Counterexample to Causation
A counterexample to causation might show that the supposed cause occurs without the effect, undermining the argument's logic.
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Temporal Order in Causation
Temporal order in causation requires that the cause precede the effect in time, a key criterion often missing in flawed arguments.
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Multiple Causes
Multiple causes refer to situations where several factors contribute to an effect, making it hard to isolate a single causal relationship.
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Necessary vs. Sufficient Cause
A necessary cause must be present for an effect to occur, while a sufficient cause alone can produce it, both concepts relevant in dissecting arguments.
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Sufficient Cause
A sufficient cause is one that, by itself, guarantees the effect, helping to strengthen causal claims in GMAT reasoning problems.
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Necessary Cause
A necessary cause is required for an effect but not enough on its own, a nuance that can weaken or support arguments depending on context.
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Causal Mechanism
A causal mechanism explains how one variable leads to another, and its absence can indicate that a correlation is not truly causal.
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Random Variation
Random variation can create apparent correlations that mimic causation, so GMAT questions often test recognition of this possibility.
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Longitudinal Study
A longitudinal study tracks variables over time, potentially providing better evidence for causation than cross-sectional data.
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Cross-Sectional Study
A cross-sectional study examines variables at a single point, often revealing correlations but rarely establishing causation due to its snapshot nature.
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Bias in Causal Studies
Bias in causal studies, such as selection bias, can distort results, leading to incorrect assumptions about correlations and causation.
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Extraneous Variables
Extraneous variables are outside factors that could influence results, and controlling for them is essential to claim causation.
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Causation in Persuasive Arguments
In persuasive arguments on the GMAT, causation is used to build conclusions, but questions test if it's logically sound or based on faulty correlations.
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Disproving Causation
Disproving causation involves showing that the effect occurs without the supposed cause or that other factors explain the relationship.
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Causal Hypothesis
A causal hypothesis proposes a cause-effect link, and GMAT questions may require evaluating its validity based on provided evidence.
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Correlation Threshold
A correlation threshold is the point at which a relationship is considered strong, but even high thresholds don't guarantee causation.
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False Causality
False causality is the mistaken belief that a correlation represents a true cause-effect relationship, a trap in many GMAT critical reasoning questions.
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Causal vs. Associative Language
Causal language implies direct influence, while associative language describes links, and distinguishing them is key in argument analysis.
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Evidence for Causation
Evidence for causation includes consistent patterns, temporal precedence, and elimination of alternatives, all of which must be present for a strong claim.
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Correlation in Data Sets
Correlation in data sets shows patterns across observations, but GMAT problems often require caution against overinterpreting them as causal.
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Advanced Causal Reasoning
Advanced causal reasoning involves considering complex interactions, like feedback loops, to accurately assess arguments on the GMAT.