Spurious correlation refers to a misleading statistical relationship between two variables that appears meaningful but is caused by a third factor or chance.
What Is a Spurious Correlation?
In social science research, a spurious correlation occurs when two variables appear to be related, but their connection is either accidental or driven by a third, unmeasured factor. In other words, the relationship is not genuine—it just looks like it is.
Researchers often discover correlations between variables while analyzing data. However, not all correlations mean that one variable influences the other. Sometimes, the observed association is a coincidence, or both variables are being influenced by a different variable altogether. That’s what makes it spurious—false or misleading.
Understanding and detecting spurious correlations is important because social scientists want to find meaningful, causal relationships, not just patterns that happen by accident.
Why Spurious Correlation Matters in Social Science
Social science research is often focused on understanding how one thing affects another—such as how education impacts income, or how media exposure shapes political beliefs. If researchers mistake a spurious correlation for a real connection, they can draw incorrect conclusions, design flawed policies, or mislead the public.
For example, just because ice cream sales increase around the same time as crime rates rise does not mean that eating ice cream causes crime. A third variable—hot weather—could be influencing both. People buy more ice cream in the summer, and hot days might also lead to more social activity and potentially more crime. This is a classic example of a spurious correlation.
Identifying these kinds of false links is crucial to building accurate theories, conducting fair evaluations, and making evidence-based decisions.
Characteristics of a Spurious Correlation
Spurious correlations usually have three common features:
- A visible statistical relationship between two variables.
- No actual causal connection between them.
- A third variable (confounder) or external factor explains the relationship.
Sometimes, chance alone can produce correlations. When a dataset is large and complex, some variables will seem connected just by coincidence. That’s why researchers need to use critical thinking, theory, and strong research design to avoid being misled.
Examples of Spurious Correlation
Example 1: Shoe Size and Reading Ability (Education)
Suppose a researcher finds a positive correlation between children’s shoe size and their reading ability. At first glance, it might look like having bigger feet helps kids read better. But this is a spurious correlation.
The true explanation is age. Older children tend to have larger shoe sizes and better reading skills simply because they are more developed. Age is the third variable that explains both.
Example 2: Number of Churches and Crime Rates (Sociology)
Imagine a study shows that cities with more churches have higher crime rates. This might suggest that religious buildings attract crime, but this conclusion would be misleading.
The real factor here is population size. Larger cities tend to have more churches and more crime just because there are more people. The observed correlation is spurious because population drives both variables.
Example 3: Margarine Consumption and Divorce Rates (Psychology/Humor)
A famously cited spurious correlation showed that as margarine consumption in Maine decreased, so did the state’s divorce rate. The correlation was strong—but totally meaningless.
This is an example of a nonsensical spurious correlation. It shows how purely mathematical relationships can appear convincing even when they make no sense in the real world.
How to Identify a Spurious Correlation
Recognizing a spurious correlation requires careful thinking, background knowledge, and good research practices. Here are some strategies researchers use:
1. Use Theory to Guide Interpretation
Theories help researchers decide whether a correlation makes logical sense. If two variables seem linked but no theory supports their connection, the correlation might be spurious.
Example: A theory about social learning might explain a link between peer behavior and juvenile crime, but not a link between soda prices and political views.
2. Control for Confounding Variables
A confounding variable is a third factor that influences both variables in a correlation. To detect spuriousness, researchers can include these confounders in their analysis.
Example: If age is causing the correlation between shoe size and reading, adding age to the model will make the original correlation disappear.
3. Use Experimental or Quasi-Experimental Designs
Experiments can help avoid spurious correlations by randomly assigning participants to conditions. This reduces the chances that other variables are causing the observed effect.
When experiments are not possible, quasi-experimental designs (like matched groups or time series analysis) can help reduce the influence of confounders.
4. Look for Temporal Order
Causal relationships usually happen in a certain order: the cause comes before the effect. If variable A comes after variable B, it can’t be causing it.
Example: If income rises before education increases, then income likely didn’t result from education. This may suggest a spurious link or a different causal pathway.
5. Replicate the Findings
Spurious correlations may appear in one dataset but not in others. Replicating the study with different data or in different populations can reveal whether the relationship holds up.
If a correlation disappears in new data, it was likely spurious or limited to a specific situation.
Common Sources of Spurious Correlation
Spurious correlations can come from a variety of sources in research. Here are some of the most common:
Sampling Bias
If the sample is not representative of the larger population, observed correlations may not be valid. An unbalanced sample can lead to misleading associations.
Measurement Error
When variables are measured poorly, noise in the data can create random correlations. Improving measurement tools reduces this risk.
Multiple Comparisons
The more comparisons researchers make, the more likely they are to find correlations just by chance. This is known as the multiple comparisons problem or p-hacking.
Seasonal or Temporal Effects
Some correlations arise because two variables follow similar seasonal or time-related trends.
Example: Sales of sunscreen and traffic accidents may both increase in the summer, but this doesn’t mean sunscreen causes crashes.
Hidden Variables
Often, a third variable that is not measured (a lurking variable) can explain the relationship between two others.
Example: Health behavior and income may seem correlated, but education level could be the hidden variable affecting both.
Consequences of Misinterpreting Spurious Correlations
Believing a spurious correlation is real can lead to:
- Faulty scientific conclusions
- Misguided public policies
- Wasted resources in program implementation
- Misleading headlines and misinformation
In social science, this can mean everything from designing the wrong educational intervention to falsely blaming a group or behavior for a social problem.
Careful research design and critical thinking are essential to prevent these mistakes.
Spurious Correlation vs. Causation
A common saying in research is: correlation does not equal causation. Just because two variables move together doesn’t mean one causes the other.
Spurious correlation is a specific case where the correlation is false or misleading. It looks like there’s a connection, but there’s no real causation between the variables.
To establish causation, researchers must:
- Show that the cause comes before the effect
- Rule out alternative explanations
- Demonstrate a consistent and logical connection
Without this evidence, any observed relationship could be spurious.
How Social Scientists Avoid Spurious Correlations
Researchers across social science disciplines take steps to avoid and correct for spuriousness.
Sociology
Sociologists studying neighborhood crime might control for poverty levels, education, and population density to avoid spurious findings.
Psychology
Psychologists often use randomized experiments or matched groups to separate real effects from spurious patterns.
Political Science
Political scientists may use panel data or fixed-effects models to account for time-invariant variables and reduce spurious results.
Education
Education researchers often rely on longitudinal studies, tracking students over time to avoid confusing correlation with causation.
Criminology
Criminologists use statistical controls and triangulation (combining different methods) to validate their findings and reduce spuriousness.
Conclusion
Spurious correlations are misleading statistical relationships that appear real but are caused by chance, third variables, or flawed research design. Recognizing and avoiding them is one of the most important skills in social science research.
By applying theory, using careful controls, designing strong studies, and staying skeptical of surface-level patterns, researchers can protect their work from being misled by false correlations.
Spurious relationships may look convincing in charts or data tables, but without solid evidence, they are just noise in the research process. Social scientists aim to uncover the signal—not be distracted by illusions.
Glossary Return to Doc's Research Glossary
Last Modified: 03/27/2025