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5 Medical Mistakes Caused by Thinking 'Correlation' Is 'Causation' — No. 3 Surprises Many. Have you heard of the term 'observational interpretation fallacy'? It is a cognitive bias that causes many experts to mistakenly assume a correlation as a causation, leading to incorrect public health policies. This article reveals five of the most dramatic examples in medical history, where observational data has misled us — and evidence from randomized trials later corrected everything.. 1. The 'Numerology Illusion' That Changed Medicine
In 2024, a study in the Journal of Evaluation in Clinical Practice introduced a new term: observational interpretation fallacy. It refers to our brain's tendency to see a relationship in observational data — like studies that only observe without strict controls — and then assume it as a cause-and-effect relationship. This is not a minor issue. In the medical world, this mistake has changed clinical guidelines, public health policies, and medical practices, sometimes with harmful effects on patients and wasted resources. Imagine: one number in a table, one clear graph, can make doctors around the world change how they treat — only to be denied years later by more rigorous randomized trials.
2. Hormone Therapy Case: From 'Heart Savior' to 'Silent Killer'
One of the most famous examples occurred in the 1990s. Large observational studies such as the Nurses' Health Study found that women taking hormone replacement therapy HRT after menopause had a lower risk of heart disease. This relationship seemed so strong that many doctors began widely recommending HRT to protect the heart. However, when controlled randomized trials RCTs — such as the Women's Health Initiative — were conducted, the results were surprising: HRT actually increased the risk of heart attacks, strokes, and blood clots. Millions of women had taken this medication for years based on a wrong interpretation. The observational interpretation fallacy caused a real public health tragedy.
3. Vitamin E: A False 'Heart Protector' That Was Popular in the Market
Another example involved vitamin E. In the early 2000s, many observational studies showed that people who took vitamin E supplements had lower rates of heart disease. This relationship seemed logical — vitamin E is an antioxidant, so it should protect cells from damage. As a result, vitamin E supplements became popular in the market, with many people believing they could save their hearts. But when RCTs were conducted, the results were the opposite: high-dose vitamin E provided no benefit, and in some studies, it was linked to an increased risk of heart failure. This is another case where we mistakenly assumed 'correlation' as 'causation' simply because observational data seemed convincing.
4. Beta-Carotene and Smoking: The Irony of a 'Protector' That Kills
Beta-carotene — a type of antioxidant found in carrots and vegetables — was once considered a cancer protector. Observational studies showed that people who ate a lot of fruits and vegetables and had high levels of beta-carotene in their blood had a lower risk of lung cancer. So, researchers decided to test beta-carotene in supplement form on smokers — a group at highest risk. But RCTs revealed a bitter truth: smokers who took beta-carotene actually had a higher risk of lung cancer than those on a placebo. The observational interpretation fallacy here was not only misleading but also dangerous. It teaches us that what appears to protect in observational data may not be the same in practice.
5. How Can We Keep Falling Into This Trap? And What Can Be Done?
Every day, we are exposed to news about foods, lifestyles, or medicines that are 'proven' to be good or bad — based on observational studies. From coffee that 'extends life' to chocolate that 'reduces blood pressure,' all of these may just be illusions of correlation. The observational interpretation fallacy occurs because we cannot control all other factors that might influence the outcome. People who eat a lot of vegetables may also be more active, smoke less, or have higher education. So, how can we protect ourselves? One way is to give more priority to evidence from RCTs — the 'gold standard' in medical science. However, RCTs are not perfect; they are expensive, difficult, and sometimes unethical. Therefore, we need to be critical readers: don't believe every 'study' that goes viral, and understand that 'related' is not the same as 'causing.' This is an important lesson brought by this new term — a lesson that can save lives if we choose to listen.
Rujukan: Observational interpretation fallacy — Wikipedia https://en.wikipedia.org/wiki/Observational interpretation fallacy