observational interpretation fallacy

Researchers aiming to use observational data to infer causation must control for confounding variables, as failing to do so can lead to spurious correlations, which then lead to mistakenly inferring causal relationships from mere associations between variables.{{cite journal|date=21 January 2025 |doi=10.1111/ele.70023 |first1=Jarrett |first2=Laura |language=en |last1=Byrnes |last2=Dee |journal=Ecology Letters |number=28 |title=Causal Inference With Observational Data and Unobserved Confounding Variables |volume=28 |pages=e70023 |pmid=39836442 |pmc=11750058 |quote=The major challenge using 'observational data for causal inference' is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders—known or unknown—lead to statistical bias, creating spurious correlations and 'masking true causal relationships...}}{{efn|1=The differentiation between association and causation is a significant challenge in medical research, often further complicated by cognitive biases that erroneously interpret coincidental observational data as indicative of causality.}} Associations in observational studies may not indicate causation and can arise due to random error (chance), systematic error (bias), or confounding variables influencing both the predictor and outcome.{{cite journal|date=January 2023 |doi=10.1097/01.NAJ.0000911536.51764.47 |first1=Bernadette |language=en |last1=Capili |journal=American Journal of Nursing |number=1 |title= Improving the Validity of Causal Inferences in Observational Studies. |volume=123 |pages=45–49 |pmid=36546389 |pmc=10036082 |quote=

It is important to note that some associations found in an observational study do not represent cause and effect. However, there are well-recognized explanations for associations between a predictor and outcome in such studies (Hulley, 2013). These occur by chance (random error), bias (systematic error), and confounding variables.}}

One of the primary challenges in observational studies is bias due to confounding.{{cite journal|date=February 2012 |doi=10.1055/s-0031-1298595 |first1=Andrea |first2=Joseph |first3=Erika |language=en |last1=Skelly |last2=Dettori |last3=Brodt |journal=Evidence-Based Spine-Care Journal |number=1 |pages=9–12 |title=Assessing bias: the importance of considering confounding |volume=3|pmid=23236300 |pmc=3503514 }} Confounding occurs when an unmeasured or unaccounted variable influences both the exposure and the outcome, creating a false appearance of a causal relationship. For example, in studies linking smoking to higher rates of suicide, the hypothesis arose because smokers were disproportionately represented among suicide cases. Observational data showed that individuals who committed suicide were more likely to be smokers compared to the general population.{{cite journal|access-date=2025-01-08 |date=1994-10-08 |doi=10.1136/bmj.309.6959.901 |first1=R. |first2=R. |first3=K. |language=en |last1=Doll |last2=Peto |last3=Wheatley |journal=BMJ |number=6959 |pages=901–911 |title=Mortality in relation to smoking: 40 years' observations on male British doctors |url=https://www.bmj.com/content/309/6959/901 |volume=309|pmid=7755693 |pmc=2541142 }}{{cite journal|access-date=2025-01-08 |date=1980-04-05 |doi=10.1136/bmj.280.6219.967 |first1=R. |first2=R. |first3=B. |language=en |last1=Doll |last2=Gray |last3=Hafner |journal=Br Med J |number=6219 |pages=967–971 |title=Mortality in relation to smoking: 22 years' observations on female British doctors. |url=https://www.bmj.com/content/280/6219/967 |volume=280|pmid=7417764 |pmc=1601142 }}{{cite journal|access-date=2025-01-08 |date=1993-05-01 |doi=10.1016/0895-4356(93)90025-V |first1=Aage |first2=Dag |first3=Inger |language=English |last1=Tverdal |last2=Thelle |last3=Stensvold |journal=Journal of Clinical Epidemiology |number=5 |pages=475–487 |title=Mortality in relation to smoking history: 13 years' follow-up of 68,000 Norwegian men and women 35–49 years |url=https://linkinghub.elsevier.com/retrieve/pii/089543569390025V |volume=46|pmid=8501474 }} This led to the assumption that smoking itself might be a risk factor for suicidal behavior.{{cite journal|access-date=2025-01-08 |date=1994-08-13 |doi=10.1136/bmj.309.6952.445 |first1=E. |first2=P. |first3=J. |language=en |last1=Variainen |last2=Puska |last3=Pekkanen |journal=BMJ |number=6952 |pages=445–447 |title=Serum cholesterol concentration and mortality from accidents, suicide, and other violent causes |url=https://www.bmj.com/content/309/6952/445 |volume=309|pmid=7920128 |pmc=2540928 }}{{cite journal|date=February 1993 |doi=10.2105/AJPH.83.2.249 |first1=D |first2=S J |first3=G A |last1=Hemenway |last2=Solnick |last3=Colditz |journal=American Journal of Public Health |number=2 |pages=249–251 |title=Smoking and suicide among nurses. |volume=83|pmid=8427332 |pmc=1694571 }} However, further investigations revealed that this association was likely due to confounding factors, such as underlying mental health conditions that are more prevalent among smokers.{{cite journal|access-date=2025-01-08 |date=September 1992 |doi=10.1016/0140-6736(92)92242-8 |first1=G. |first2=A.N. |first3=J.D. |last1=Davey Smith |last2=Phillips |last3=Neaton |journal=The Lancet |number=8821 |pages=709–712 |title=Smoking as "independent" risk factor for suicide: illustration of an artifact from observational epidemiology? |url=https://doi.org/10.1016/0140-6736(92)92242-8 |volume=340}} These conditions, including depression and anxiety, could independently contribute to both smoking behavior and an increased risk of suicide, thereby creating a false impression of a direct causal link between smoking and suicide.

Cognitive biases can exacerbate the misinterpretation of observational data. These biases lead researchers, clinicians, or policymakers to focus on information that aligns with pre-existing beliefs while disregarding conflicting evidence. This creates a feedback loop where preliminary conclusions—often derived from confounded observational data—are reinforced by selective interpretation or the improper use of causal language. Terms like "association," even when accurately used, may still be misinterpreted as implying causation, further amplifying the issue. One prominent example is the post hoc ergo propter hoc fallacy — a Latin phrase meaning "after this, therefore because of this." This fallacy occurs when a temporal sequence is mistaken for a causal relationship, leading to the erroneous assumption that if one event follows another, the former must have caused the latter.{{cite journal|access-date=2025-01-08 |date=April 2017 |doi=10.1176/appi.ajp.2016.16111320 |first=E. Jane |last=Costello |journal=American Journal of Psychiatry |number=4 |pages=305–306 |title=Post Hoc, Ergo Propter Hoc |url=https://psychiatryonline.org/doi/10.1176/appi.ajp.2016.16111320 |volume=174|pmid=28366086 }} Such reasoning can be deceptive, as the apparent connection between events may overlook critical variables that could explain the observed outcomes.

Another key contributor is confirmation bias, which involves systematic deviations from rational judgment. This bias leads individuals to focus on information that supports their preconceptions while dismissing or undervaluing evidence to the contrary.{{cite book|access-date=2025-01-08 |date=1995-01-01 |doi=10.1016/s0079-7421(08)60315-1 |first=Joshua |last=Klayman |pages=385–418 |publisher=Academic Press |title=Varieties of Confirmation Bias |series=Psychology of Learning and Motivation |url=https://linkinghub.elsevier.com/retrieve/pii/S0079742108603151 |volume=32|isbn=978-0-12-543332-7 }} For example, researchers may selectively interpret uncertain data as supportive of their hypotheses, reinforcing initial assumptions even when contradictory evidence emerges. This selective perception creates a self-reinforcing cycle, where flawed conclusions persist despite being challenged or invalidated by new findings.

The observational interpretation fallacy is the cognitive bias where correlations identified in observational studies are erroneously interpreted as evidence of causality. This misinterpretation can significantly influence clinical guidelines and healthcare practices, potentially compromising patient safety and the efficient allocation of resources. The fallacy often manifests when the inherent limitations of observational studies, such as confounding factors and the lack of controlled interventions, are overlooked in the rush to apply findings to clinical practice.

The observational interpretation fallacy differs from individual cognitive biases by influencing the collective judgment within the scientific community. This bias arises not solely from observing coinciding events but from the misinterpretation of these observations in scientific literature. As a result, the fallacy can lead to the establishment of clinical practices and guidelines that lack a foundation in rigorously tested evidence.

Unlike individual biases such as confirmation bias, the observational interpretation fallacy operates on a broader scale, affecting the direction of medical research and the implementation of healthcare interventions. By shaping scientific consensus and influencing policy decisions, this fallacy can perpetuate flawed interpretations of observational data, resulting in widespread implications for clinical practice and resource allocation.

Sixteen major examples have been identified in the scientific literature where the erroneous interpretation of observational data led to significant consequences in clinical practice and health policy.

From 1956 to 1983, Bendectin was a widely prescribed medication in the United States, with up to 25% of pregnant women using it at its peak. However, in 1980, observational studies erroneously linked Bendectin to birth defects,{{cite journal|access-date=2025-01-08 |date=1974-09-21 |doi=10.1016/S0140-6736(74)93258-9 |first1=Bruce |first2=Nancy |first3=Richard |language=English |last1=Armstrong |last2=Stevens |last3=Doll |journal=The Lancet |number=7882 |pages=672–675 |title=Retrospective Study of the Association Between Use of Rauwolfia Derivatives and Breast Cancer in English Women |url=https://linkinghub.elsevier.com/retrieve/pii/S0140673674932589 |volume=304|pmid=4142956 }}{{cite journal|access-date=2025-01-08 |date=1974-09-21 |doi=10.1016/S0140-6736(74)93259-0 |first1=O. P. |first2=S. |first3=Liisa |language=English |last1=Heinonen |last2=Shapiro |last3=Tuominen |journal=The Lancet |number=7882 |pages=675–677 |title=Reserpine Use in Relation to Breast Cancer |url=https://linkinghub.elsevier.com/retrieve/pii/S0140673674932590 |volume=304|pmid=4142957 }} sparking widespread concern and a flood of lawsuits against its manufacturer, Merrell. The legal challenges dramatically increased the company's insurance costs to $10 million annually—far exceeding the drug's $3 million revenue—ultimately forcing its withdrawal from the market.{{cite journal|access-date=2025-01-08 |date=2014-03-20 |doi=10.1056/NEJMp1316042 |first1=Shelley R. |first2=Rhonda |first3=Theresa van der |last1=Slaughter |last2=Hearns-Stokes |last3=Vlugt |journal=New England Journal of Medicine |number=12 |pages=1081–1083 |title=FDA Approval of Doxylamine–Pyridoxine Therapy for Use in Pregnancy |url=https://www.nejm.org/doi/10.1056/NEJMp1316042 |volume=370|pmid=24645939 }}

The absence of Bendectin had serious consequences: hospitalizations for pregnancy-related nausea doubled, highlighting the drug's unique effectiveness.{{cite journal|access-date=2025-01-08 |date=2014-03-20 |doi=10.1056/NEJMp1316042 |first1=Shelley R. |first2=Rhonda |first3=Theresa van der |last1=Slaughter |last2=Hearns-Stokes |last3=Vlugt |journal=New England Journal of Medicine |number=12 |pages=1081–1083 |title=FDA Approval of Doxylamine–Pyridoxine Therapy for Use in Pregnancy |url=https://www.nejm.org/doi/10.1056/NEJMp1316042 |volume=370|pmid=24645939 }} Years later, subsequent research debunked the teratogenic claims,{{cite journal|access-date=2025-01-08 |date=1985-10-01 |doi=10.1001/archinte.1985.00360100139023 |first=Ralph I. |language=en |last=Horwitz |journal=Archives of Internal Medicine |number=10 |pages=1873–1875 |title=Exclusion Bias and the False Relationship of Reserpine and Breast Cancer |url=http://archinte.jamanetwork.com/article.aspx?doi=10.1001/archinte.1985.00360100139023 |volume=145|pmid=4037948 }} and the FDA reapproved Bendectin in 2014.{{cite journal|access-date=2025-01-08 |date=2014-03-20 |doi=10.1056/NEJMp1316042 |first1=Shelley R. |first2=Rhonda |first3=Theresa van der |last1=Slaughter |last2=Hearns-Stokes |last3=Vlugt |journal=New England Journal of Medicine |number=12 |pages=1081–1083 |title=FDA Approval of Doxylamine–Pyridoxine Therapy for Use in Pregnancy |url=https://www.nejm.org/doi/10.1056/NEJMp1316042 |volume=370|pmid=24645939 }}

One of the most notable examples of misinterpreted observational data is the widespread adoption of hormone replacement therapy (HRT) to alleviate menopausal symptoms and reduce cardiovascular disease risk. This practice was initially driven by observational studies suggesting a lower incidence of heart disease among women using HRT compared to non-users.{{cite journal|access-date=2025-01-08 |date=1993-04-15 |doi=10.1056/NEJM199304153281501 |first1=Azmi A. |first2=Aaron R. |first3=Alice |last1=Nabulsi |last2=Folsom |last3=White |journal=New England Journal of Medicine |number=15 |pages=1069–1075 |title=Association of Hormone-Replacement Therapy with Various Cardiovascular Risk Factors in Postmenopausal Women |url=https://www.nejm.org/doi/full/10.1056/NEJM199304153281501 |volume=328|pmid=8384316 }}{{cite journal|access-date=2025-01-08 |date=1992-12-15 |doi=10.7326/0003-4819-117-12-1016 |first1=Deborah |first2=Susan M. |first3=Diana B. |last1=Grady |last2=Rubin |last3=Petitti |journal=Annals of Internal Medicine |number=12 |pages=1016–1037 |title=Hormone Therapy To Prevent Disease and Prolong Life in Postmenopausal Women |url=https://www.acpjournals.org/doi/10.7326/0003-4819-117-12-1016 |volume=117|pmid=1443971 }}{{cite journal|access-date=2025-01-08 |date=1997-06-19 |doi=10.1056/NEJM199706193362501 |first1=Francine |first2=Meir J. |first3=Graham A. |last1=Grodstein |last2=Stampfer |last3=Colditz |journal=New England Journal of Medicine |number=25 |pages=1769–1776 |title=Postmenopausal Hormone Therapy and Mortality |url=https://www.nejm.org/doi/full/10.1056/NEJM199706193362501 |volume=336|pmid=9187066 }} These findings were interpreted as evidence of a causal relationship, leading to the broad prescription of HRT without rigorous evaluation.

Early warnings from randomized trials that challenged this assumption were met with skepticism.{{cite journal|access-date=2025-01-08 |date=2001-11-06 |doi=10.1161/circ.104.19.2256 |first1=Michael E. |first2=Richard H. |last1=Mendelsohn |last2=Karas |journal=Circulation |number=19 |pages=2256–2259 |title=The Time Has Come to Stop Letting the HERS Tale Wag the Dogma |url=https://www.ahajournals.org/doi/10.1161/circ.104.19.2256 |volume=104|pmid=11696459 }} However, the landmark Women's Health Initiative (WHI) randomized clinical trial definitively overturned the prevailing belief. The WHI trial demonstrated that HRT not only failed to offer cardiovascular protection but also significantly increased the risks of breast cancer, stroke, and blood clots. This dramatic reversal necessitated a complete overhaul of clinical guidelines for HRT use, highlighting the risks of relying on observational data alone to inform healthcare practices.

Observational data once linked antioxidants such as vitamins A, C, and E to a reduced risk of cancer, fueling widespread recommendations for supplementation.{{cite journal|access-date=2025-01-08 |date=1991-01-01 |doi=10.1093/ajcn/53.1.265S |first1=HB |first2=KF |first3=M |last1=Stähelin |last2=Gey |last3=Eichholzer |journal=The American Journal of Clinical Nutrition |number=1 |pages=265S–269S |title=β-Carotene and cancer prevention: the Basel Study |url=https://linkinghub.elsevier.com/retrieve/pii/S0002916523170869 |volume=53|pmid=1985397 }} However, randomized trials like the Beta-Carotene and Retinol Efficacy Trial (CARET){{cite journal|access-date=2025-01-08 |date=1996-05-02 |doi=10.1056/NEJM199605023341802 |first1=Gilbert S. |first2=Gary E. |first3=Mark D. |last1=Omenn |last2=Goodman |last3=Thornquist |journal=New England Journal of Medicine |number=18 |pages=1150–1155 |title=Effects of a Combination of Beta Carotene and Vitamin A on Lung Cancer and Cardiovascular Disease |url=https://www.nejm.org/doi/full/10.1056/NEJM199605023341802 |volume=334|pmid=8602180 }} and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC Study){{cite journal|access-date=2025-01-08 |date=1996-11-06 |doi=10.1093/jnci/88.21.1560 |first1=Demetrius |first2=Olli P. |first3=Philip R. |last1=Albanes |last2=Heinonen |last3=Taylor |journal=JNCI: Journal of the National Cancer Institute |number=21 |pages=1560–1570 |title=α-Tocopherol and β-Carotene Supplements and Lung Cancer Incidence in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: Effects of Base-line Characteristics and Study Compliance |url=https://academic.oup.com/jnci/article-abstract/88/21/1560/928511?redirectedFrom=fulltext |volume=88|pmid=8901854 }} revealed not only a lack of benefit but an increased risk of cancer, particularly lung cancer among smokers. These findings prompted a reevaluation of antioxidant supplementation guidelines, highlighting the dangers of prematurely endorsing interventions based on observational studies.

Randomized controlled trials (RCTs) are considered the gold standard in medical research for establishing causality. By randomly assigning participants to either an intervention or control group, RCTs ensure that variables such as age, health status, and lifestyle are evenly distributed between groups. This randomization creates two comparable groups, making the intervention the only meaningful difference, which allows researchers to isolate cause-and-effect relationships. Unlike observational studies, which can only identify associations and are subject to confounding factors, RCTs provide reliable evidence by eliminating bias and external influences. While they are not always feasible due to ethical, logistical, or financial constraints, their ability to rigorously test interventions makes them the foundation of evidence-based medicine.

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Category:Fallacies

Category:Causal fallacies

Category:Cognitive biases