misanalyses

Misanalyses are analyses or conclusions that are incorrect because of flaws in the analytical process, the interpretation of evidence, or the application of methods. The term is used across disciplines to describe situations where the result of an analysis misrepresents reality, often due to methodological shortcomings rather than deliberate deception. Misanalyses can arise from errors in data handling, model choice, or reading results through inappropriate conceptual lenses.

Common causes include poor data quality, sampling bias, measurement error, and missing data; inappropriate or misapplied statistical methods; overfitting and data dredging; confounding or selection bias; and cognitive biases such as confirmation bias. Misanalyses may also reflect misinterpretations of probabilistic results, such as inferring causation from correlation or neglecting base rates.

Domains frequently affected include scientific research, data science and analytics, history and archaeology, journalism, forensics, and law. Examples include drawing causal inferences from observational studies without ruling out confounding variables, ecological or Simpson’s paradox scenarios, or misreading p-values as definitive proof without considering practical significance or replication.

Consequences can be significant, ranging from erroneous scientific claims and misguided policies to reputational damage and harm in medical or legal contexts. The uncertainty inherent in data and models means misanalyses can propagate if unrevised or unchallenged.

Prevention emphasizes rigorous methodology and transparency: preregistration of analyses, robust study design, appropriate statistical methods, cross-validation, sensitivity analyses, replication, data sharing, thorough peer review, and clear reporting of limitations. Cultural emphasis on methodological education and open critique also helps reduce misanalyses.