Overinterpreted

Overinterpreted is an adjective describing a claim or interpretation that asserts more significance than warranted by the evidence. It often involves inferring hidden meanings, patterns, or causal links from data, events, or texts that do not justify such conclusions. The noun form is overinterpretation.

In science, overinterpretation occurs when researchers extrapolate beyond what their data support, treat correlation as causation, or ignore alternative explanations. In media and public discourse, it shows up as sensational headlines or definitive statements based on limited studies or selective reporting. In criticism, audiences may read meanings into a work that were not intended by the creator.

Causes include cognitive biases such as confirmation bias and apophenia, limitations of small samples, and the pressure to tell a compelling story. Methodological flaws like p-hacking and multiple comparisons can also inflate perceived significance. Communication problems, including hedged language being dropped, contribute as well.

Prevention relies on rigorous study design, preregistration, transparent reporting of uncertainties, and emphasis on effect sizes and confidence intervals rather than slogans. Replication, peer review, and clear distinctions between hypothesis, correlation, and causation help guard against overinterpretation. Encouraging multiple perspectives and restraint in drawing conclusions also reduces risk.

Examples include claiming causation from a correlational study, cherry-picking supporting data, or projecting universal themes from a single example. Related concepts include hindsight bias, confirmation bias, apophenia, and the adage that correlation does not imply causation.