The cholera outbreak of 1854 – The Correlation/Causation Fallacy: When Coincidence Becomes Conviction

The fallacy of assuming that because two events occur together, one must cause the other.

In the 1850s, London was in the grip of a cholera epidemic that was killing thousands of people. The medical establishment was convinced they understood the cause: bad air, or “miasma,” that rose from sewers and garbage. This theory made perfect sense to them because they had observed a clear correlation—cholera seemed to be more common in areas with bad smells and poor sanitation.

The cholera pandemic of 1854 was London’s third major outbreak in less than two decades. The disease struck with terrifying speed and brutality—victims could be healthy in the morning and dead by evening. The 1854 outbreak alone killed over 10,000 people in London, with the working-class neighborhoods of Soho bearing the brunt of the devastation. In some areas, the death rate reached 12.8% of the population.

The Miasma Theory: A Logical Illusion

The miasma theory dominated medical thinking throughout the 19th century, and it seemed to make perfect sense. The correlation between bad air and disease was undeniable—cholera, yellow fever, and other epidemic diseases were indeed more common in areas with poor sanitation and foul odors. The Thames River was an open sewer, the air in poor neighborhoods reeked of decay, and disease flourished in these same areas.

The theory was supported by respected medical authorities across Europe. Florence Nightingale, the famous nurse, was a strong advocate of the miasma theory. The British government’s Public Health Act of 1848 was based entirely on miasma theory, focusing on eliminating bad air and foul odors from cities. Medical schools taught miasma theory as established fact.

The correlation seemed so obvious that questioning it appeared foolish. Wealthy neighborhoods with cleaner air had lower disease rates. Poor neighborhoods with foul air had higher disease rates. The logic was straightforward: bad air caused disease. What the medical establishment failed to recognize was that correlation does not imply causation, and that multiple factors could explain the same observed relationship.

The miasma theory also fit with the prevailing understanding of disease transmission. Before the development of germ theory, people needed explanations for why diseases spread through populations. The idea that invisible particles in the air could cause illness was more plausible than the idea that microscopic organisms in water could do the same.

The Outsider’s Observation

Dr. John Snow, a London physician, noticed a different correlation. In the Soho district, cholera cases seemed to cluster around a particular water pump on Broad Street. Most of the victims had used that pump, while people who used other water sources remained relatively healthy. Snow hypothesized that contaminated water, not bad air, was spreading the disease.

Snow was not part of the medical establishment’s inner circle. He was an anesthesiologist who had developed innovative techniques for administering ether and chloroform, but he was not considered an authority on epidemic diseases. His outsider status may have actually helped him see patterns that the establishment missed, as he wasn’t intellectually committed to the miasma theory.

Snow’s investigation was methodical and scientific. He created detailed maps showing the locations of cholera deaths in relation to water sources. He interviewed families of victims to determine their water consumption habits. He analyzed the chemical composition of water from different sources. His approach was revolutionary for its time—he was using what we now call epidemiological methods decades before epidemiology became a recognized field.

The Broad Street pump served a densely populated area of Soho, and Snow’s mapping showed that cholera deaths clustered tightly around the pump. The further from the pump, the fewer the deaths. This spatial correlation was much tighter than the correlation between bad air and disease that the medical establishment relied upon.

Snow also noticed anomalies that the miasma theory couldn’t explain. A brewery near the Broad Street pump had no cholera cases among its workers—because the brewery workers drank beer instead of water from the pump. A workhouse in the area had only a few cases—because it had its own water supply. These anomalies made no sense under the miasma theory but were perfectly consistent with Snow’s water contamination hypothesis.

The Establishment’s Resistance

The medical establishment rejected Snow’s theory completely. They couldn’t see how their correlation could be wrong—bad air and cholera clearly went together. They dismissed Snow’s water pump theory as absurd. After all, many people used the Broad Street pump without getting sick, and some people who got cholera lived far from the pump.

The resistance to Snow’s theory was not just intellectual—it was institutional and professional. The miasma theory was taught in medical schools, endorsed by government health officials, and accepted by the most respected physicians of the time. Accepting Snow’s theory would have meant admitting that the entire medical establishment had been wrong about a fundamental aspect of disease transmission.

The Board of Health, Britain’s central health authority, was particularly hostile to Snow’s theory. They had invested enormous resources in miasma-based public health measures and were not willing to acknowledge that their approach might be fundamentally flawed. The Board’s 1855 report on the cholera outbreak specifically rejected Snow’s water contamination theory.

The medical journal The Lancet, which was and remains one of the most prestigious medical publications, published scathing critiques of Snow’s work. They argued that his theory was “too narrow” and that he was ignoring the obvious evidence of airborne transmission. The medical establishment’s commitment to the miasma theory was so strong that they interpreted Snow’s evidence through the lens of their existing beliefs rather than allowing it to challenge those beliefs.

Professional pride also played a role in the resistance. Many physicians had built their careers on the miasma theory and had achieved recognition for their work on air quality and sanitation. Accepting Snow’s theory would have meant acknowledging that much of their professional work had been misdirected.

The Reinforcement of False Beliefs

The correlation/causation fallacy was reinforced by the medical community’s existing beliefs about disease transmission. They had been taught that diseases spread through bad air, and they had observed many cases where disease and bad air coincided. This correlation had become so deeply embedded in their thinking that they couldn’t consider alternative explanations.

The miasma theory created a powerful feedback loop that reinforced itself. Public health measures focused on eliminating bad air did sometimes reduce disease rates, but not for the reasons the medical establishment believed. Improving sanitation and air quality often involved cleaning up waste and sewage, which indirectly improved water quality as well. When disease rates declined, the medical establishment credited the miasma theory, not realizing that they had accidentally addressed the real cause.

The correlation between bad air and disease was also reinforced by social class differences. Poor neighborhoods had both worse air quality and higher disease rates, but they also had contaminated water supplies, overcrowding, malnutrition, and other factors that contributed to disease. The medical establishment focused on the most obvious correlation—bad air—while ignoring the more subtle but more important correlation with water contamination.

The miasma theory also benefited from what psychologists now call “confirmation bias”—the tendency to seek information that confirms existing beliefs while ignoring contradictory evidence. Doctors who believed in the miasma theory paid attention to cases where disease and bad air coincided while overlooking cases where they didn’t.

The Breakthrough Evidence

Snow persisted in his investigation despite the ridicule. He traced the source of the contaminated water to a broken sewer pipe that was leaking into the well that supplied the Broad Street pump. He convinced local authorities to remove the pump handle, and the cholera outbreak in that area ended almost immediately.

Snow’s investigation revealed a specific mechanism for water contamination. The well that supplied the Broad Street pump was located just three feet from a cesspit that had been leaking sewage. The “index case”—the first person to die in the outbreak—was an infant whose mother had been washing contaminated diapers in water that eventually seeped into the well. This created a direct causal chain from sewage to water supply to disease transmission.

The pump handle removal was a dramatic public health intervention. Snow had to convince the Board of Guardians of St. James’s Parish to take the unusual step of disabling a public water source based on his unproven theory. The fact that they agreed to his request, despite the medical establishment’s opposition, suggests that his evidence was compelling even to non-experts.

The immediate end of the outbreak after the pump handle removal provided what scientists call a “natural experiment”—a situation where a single variable is changed and the effects can be observed. This was powerful evidence for Snow’s theory, but it wasn’t conclusive proof, as the outbreak might have been ending for other reasons.

Snow documented his findings in detailed reports that included maps, charts, and statistical analysis. His work was methodologically sophisticated and would be considered rigorous by modern epidemiological standards. He was using scientific methods that wouldn’t become standard in medicine for several more decades.

The Persistent Denial

Even this dramatic result didn’t convince the medical establishment. They argued that the outbreak was already ending naturally, and that removing the pump handle was merely coincidental. They continued to focus on eliminating bad air rather than cleaning up water supplies.

The medical establishment’s response to Snow’s evidence reveals how powerful the correlation/causation fallacy can be. When people are deeply committed to a particular explanation for observed correlations, they will often explain away contradictory evidence rather than revise their beliefs. The pump handle removal was dismissed as coincidental because accepting it as causal would have required abandoning the miasma theory.

The Board of Health’s 1855 report acknowledged that cholera cases had clustered around the Broad Street pump but argued that this was because the pump was located in an area with particularly bad air. They maintained that the pump was a symptom, not a cause, of the underlying problem of miasma. This interpretation allowed them to acknowledge Snow’s observation while maintaining their theoretical framework.

Even when Snow published additional evidence showing that cholera rates were higher among customers of water companies that drew water from contaminated sources, the medical establishment found ways to dismiss his findings. They argued that the contaminated water companies also served areas with worse air quality, so the correlation could still be explained by the miasma theory.

The resistance to Snow’s theory was so strong that it persisted even after his death in 1858. Medical textbooks continued to teach the miasma theory, and public health policies continued to focus on air quality rather than water contamination. The correlation/causation fallacy had become so institutionalized that it took decades to overcome.

The Long Road to Acceptance

The correlation/causation fallacy continued to kill people for decades. It wasn’t until the 1880s, when Louis Pasteur’s germ theory gained acceptance, that the medical community finally understood that cholera was caused by contaminated water, not bad air. The correlation they had observed between bad air and disease was real, but the causation they had assumed was wrong—both bad air and disease were often symptoms of the same underlying problem: poor sanitation.

The acceptance of germ theory required a fundamental shift in how the medical community thought about disease causation. Instead of looking for correlations between environmental conditions and disease, they began to look for specific infectious agents that could be isolated, studied, and controlled. This shift from correlation-based to mechanism-based thinking was crucial to the development of modern medicine.

Robert Koch’s work on the cholera bacterium in the 1880s provided the definitive proof that Snow had been right. Koch was able to isolate the cholera bacterium from contaminated water and demonstrate that it caused the disease. This provided a specific causal mechanism that explained both Snow’s observations and the limitations of the miasma theory.

The vindication of Snow’s theory came too late for Snow himself, who had died in 1858 without seeing his work accepted. It also came too late for the thousands of people who died from cholera and other waterborne diseases because the medical establishment had confused correlation with causation.

The Enormous Cost

The cost of this fallacy was enormous. Tens of thousands of people died from cholera and other waterborne diseases because the medical establishment mistook correlation for causation. Resources were wasted on ineffective treatments focused on air quality while the real cause—contaminated water—was ignored.

The 1854 cholera outbreak alone killed over 10,000 people in London, and subsequent outbreaks continued to devastate the city throughout the 1850s and 1860s. The 1866 outbreak killed another 5,596 people, largely because the medical establishment was still focused on the wrong causal mechanism.

The economic costs were equally staggering. London spent enormous sums on ineffective miasma-based public health measures while neglecting water system improvements that could have prevented disease. The Great Stink of 1858, when the Thames River became so polluted that it shut down Parliament, led to massive spending on sewage treatment—but the focus was on eliminating bad smells rather than preventing water contamination.

The international impact was also significant. The miasma theory was exported to other countries, where it led to similar misallocation of resources and continued disease outbreaks. Cities around the world implemented air quality measures while neglecting water safety, perpetuating the same correlation/causation fallacy that had proved so deadly in London.

The Modern Business Parallel

The correlation/causation fallacy continues to lead us astray in business and personal decisions. When we assume that because two things happen together, one must cause the other, we often focus on the wrong solutions to real problems. Snow’s story reminds us that correlation can be a valuable clue, but it’s not proof of causation—and acting on that false proof can have deadly consequences.

Marketing Attribution: Companies often assume that because sales increase after a marketing campaign, the campaign caused the increase. This ignores other factors like seasonality, competitor actions, or economic conditions that might explain the correlation. Resources are often wasted on ineffective marketing channels because of false causal assumptions.

Performance Management: Businesses frequently assume that because productive employees share certain characteristics, those characteristics cause productivity. This leads to hiring and promotion decisions based on correlations rather than actual predictors of performance. The result is often discrimination against qualified candidates who don’t fit the correlation pattern.

Technology Adoption: Organizations often implement new technologies because they correlate with success at other companies, without understanding whether the technology actually causes the success or whether successful companies are simply more likely to adopt new technologies.

Financial Analysis: Investors often assume that because certain financial metrics correlate with stock price movements, those metrics cause the movements. This leads to investment strategies based on false causal assumptions rather than fundamental analysis.

Operational Efficiency: Companies frequently assume that because efficient operations correlate with certain practices, those practices cause efficiency. This can lead to implementing practices that are symptoms of good management rather than causes of it.

The Personal Decision Trap

In personal life, the correlation/causation fallacy can be equally misleading:

Health Decisions: People often assume that because healthy people share certain behaviors, those behaviors cause health. This can lead to adopting ineffective health practices while ignoring more important factors like genetics, environment, or access to healthcare.

Career Choices: Individuals often assume that because successful people in their field share certain characteristics, those characteristics cause success. This can lead to mimicking superficial traits rather than developing fundamental skills.

Relationship Patterns: People often assume that because happy couples share certain behaviors, those behaviors cause happiness. This ignores the possibility that happy couples engage in those behaviors because they’re happy, not the other way around.

Financial Decisions: Individuals often assume that because wealthy people make certain financial choices, those choices cause wealth. This can lead to copying investment strategies or spending patterns without understanding the underlying factors that enable wealth building.

Educational Choices: Parents often assume that because high-achieving students attend certain schools or programs, those schools cause achievement. This ignores selection effects and other factors that might explain the correlation.

The Social Media Amplification

Social media has amplified the correlation/causation fallacy in several ways:

Viral Correlations: Social media makes it easy to identify and share correlations, but much harder to establish causation. Viral posts often present correlations as definitive proof of causation without rigorous analysis.

Survivorship Bias: Social media amplifies success stories while hiding failures, creating false correlations between certain behaviors and success. People see successful individuals who share certain traits but don’t see the many unsuccessful people who share the same traits.

Echo Chambers: Social media algorithms create echo chambers where false causal beliefs are reinforced by repeated exposure to the same correlations without contrary evidence.

Instant Gratification: Social media’s emphasis on quick, shareable content favors simple causal explanations over complex ones, making correlation/causation fallacies more appealing.

Confirmation Bias: Social media makes it easy to find correlations that confirm existing beliefs while avoiding information that challenges those beliefs.

The Scientific Method’s Defense

The scientific method provides systematic defenses against the correlation/causation fallacy:

Controlled Experiments: By controlling for confounding variables, experiments can help establish causal relationships rather than just correlations.

Randomization: Random assignment helps ensure that observed correlations aren’t due to selection bias or other confounding factors.

Replication: Repeating studies helps confirm whether correlations reflect genuine causal relationships or chance occurrences.

Peer Review: The scientific community’s review process helps identify correlation/causation fallacies before they become accepted as fact.

Theoretical Frameworks: Scientific theories provide mechanisms that explain how causes produce effects, going beyond simple correlation to establish plausible causal pathways.

The Statistical Sophistication

Modern statistical methods provide tools for distinguishing correlation from causation:

Regression Analysis: Statistical techniques can help identify which correlations remain significant after controlling for other variables.

Longitudinal Studies: Following subjects over time can help establish temporal sequences that are necessary for causal relationships.

Natural Experiments: Situations where variables change for reasons unrelated to the outcome can provide evidence for causal relationships.

Instrumental Variables: Statistical techniques can help identify causal effects by using variables that affect the cause but not the outcome directly.

Causal Inference: Specialized statistical methods are designed specifically to help establish causal relationships from observational data.

The Educational Challenge

Educational institutions face the challenge of teaching students to distinguish correlation from causation:

Statistical Literacy: Students need to understand basic statistical concepts like correlation coefficients, significance testing, and confounding variables.

Critical Thinking: Students need to develop the habit of questioning apparent causal relationships and looking for alternative explanations.

Scientific Method: Students need to understand how controlled experiments and other scientific methods help establish causation.

Case Studies: Real-world examples like Snow’s cholera investigation can help students understand the practical importance of distinguishing correlation from causation.

Interdisciplinary Applications: Students need to see how the correlation/causation distinction applies across different fields, from medicine to business to social policy.

The Policy Implications

The correlation/causation fallacy has significant implications for public policy:

Evidence-Based Policy: Policies should be based on rigorous evidence of causal relationships, not just correlations between interventions and outcomes.

Program Evaluation: Government programs should be evaluated using methods that can distinguish between correlation and causation to ensure resources are allocated effectively.

Regulatory Decisions: Regulations should be based on evidence that certain factors actually cause harm, not just correlate with it.

Healthcare Policy: Medical policies should be based on evidence of causal relationships between treatments and outcomes, not just correlations.

Educational Policy: School policies should be based on evidence of what actually causes learning, not just what correlates with academic achievement.

The Psychological Roots

The correlation/causation fallacy has deep psychological roots:

Pattern Recognition: Humans are naturally good at recognizing patterns, including correlations, but this skill can lead us astray when we assume that patterns imply causation.

Narrative Bias: People prefer simple causal stories over complex explanations, making correlation/causation fallacies psychologically appealing.

Confirmation Bias: People seek information that confirms their existing beliefs about causal relationships while ignoring contradictory evidence.

Availability Heuristic: People judge causation based on how easily they can recall examples of correlation, leading to overconfidence in causal claims.

Hindsight Bias: People tend to see causal relationships as more obvious in retrospect than they actually were, making it harder to recognize correlation/causation fallacies.

The Technological Amplification

Modern technology has both amplified and provided solutions for the correlation/causation fallacy:

Big Data: Large datasets make it easier to identify correlations, but they don’t automatically establish causation. The availability of vast amounts of data can actually make the fallacy more dangerous by providing more opportunities for false pattern recognition.

Machine Learning: AI systems can identify complex correlations that humans might miss, but they can also perpetuate correlation/causation fallacies if they’re not designed with causal inference in mind.

Causal AI: New developments in artificial intelligence are specifically designed to help establish causal relationships rather than just correlations.

Data Visualization: Modern visualization tools make it easier to see correlations, but they can also make spurious correlations appear more convincing.

Automated Analysis: Statistical software can quickly identify correlations, but it requires human judgment to determine whether those correlations reflect causal relationships.

The Global Health Relevance

The correlation/causation fallacy continues to affect global health:

Disease Outbreaks: During new disease outbreaks, health officials often initially focus on correlations rather than establishing definitive causal mechanisms, sometimes leading to ineffective interventions.

Nutritional Science: Many nutritional recommendations are based on correlations between diet and health outcomes rather than rigorous causal evidence.

Environmental Health: The relationship between environmental factors and health outcomes often involves complex causal chains that are difficult to establish definitively.

Mental Health: The causes of mental health conditions are often complex and multifaceted, making it easy to mistake correlations for causations.

Health Disparities: Differences in health outcomes between populations are often attributed to specific factors based on correlation rather than definitive causal evidence.

The Economic Consequences

The correlation/causation fallacy has significant economic consequences:

Market Analysis: Investors often assume that because certain factors correlate with market movements, those factors cause the movements, leading to poor investment decisions.

Economic Policy: Government economic policies are sometimes based on correlations between policy measures and economic outcomes rather than rigorous causal analysis.

Business Strategy: Companies often base strategic decisions on correlations rather than causal relationships, leading to ineffective strategies.

Resource Allocation: Resources are often allocated based on correlations rather than causal evidence, leading to waste and inefficiency.

Innovation Investment: Research and development investments are sometimes based on correlations rather than understanding of causal mechanisms.

The Future Challenge

As we generate more data and identify more correlations, the challenge of distinguishing correlation from causation becomes even more important:

Artificial Intelligence: AI systems need to be designed with causal inference capabilities to avoid perpetuating correlation/causation fallacies.

Policy Making: Policymakers need better tools and training to distinguish between correlation and causation when making decisions.

Scientific Research: The scientific community needs to continue developing and refining methods for establishing causal relationships.

Public Education: The general public needs better education about the difference between correlation and causation to make better decisions.

Ethical Considerations: The use of correlational data to make decisions about people’s lives raises important ethical questions about fairness and accuracy.

The Continuing Relevance

John Snow’s story remains relevant because the correlation/causation fallacy continues to affect our decision-making in fundamental ways. The same psychological tendencies that led the Victorian medical establishment to confuse correlation with causation continue to influence how we think about cause and effect today.

The lesson of Snow’s investigation is not that correlations are meaningless—they can provide valuable clues about potential causal relationships. The lesson is that correlation alone is not sufficient evidence for causation, and that acting on false causal assumptions can have serious consequences.

The Ultimate Lesson

The cholera outbreak of 1854 and John Snow’s investigation provide a powerful reminder of the importance of distinguishing correlation from causation. The medical establishment’s commitment to the miasma theory, despite Snow’s evidence, shows how dangerous it can be to assume that correlation implies causation.

The cost of this fallacy was measured in thousands of lives lost to preventable disease. The lesson extends far beyond 19th-century medicine to every area of human decision-making. When we confuse correlation with causation, we often focus on the wrong solutions to real problems, wasting resources and sometimes making problems worse.

Snow’s story reminds us that the most important discoveries often come from questioning apparent correlations and looking for alternative explanations. The correlation between bad air and disease was real, but the causation was wrong. Snow’s genius was in recognizing this distinction and pursuing the evidence wherever it led, even when it contradicted the established wisdom of his time.

In our data-rich world, where we can identify correlations more easily than ever before, Snow’s lesson is more relevant than ever. The challenge is not just to identify correlations, but to understand which ones reflect genuine causal relationships and which ones are merely coincidental. The difference between correlation and causation is often the difference between effective solutions and dangerous delusions.

The correlation/causation fallacy reminds us that the world is more complex than it first appears, and that our natural tendency to see patterns and assume causation can lead us astray. The antidote is rigorous thinking, careful investigation, and the intellectual humility to question our assumptions—even when those assumptions seem obviously correct.

John Snow’s persistence in the face of institutional resistance provides a model for how to challenge false correlations and establish genuine causal relationships. His story reminds us that the truth is often more subtle than it appears, and that the most important insights often come from looking beyond the obvious correlations to find the hidden causes that actually drive the phenomena we observe.