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Submission declined on 12 March 2025 by ToadetteEdit (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner.
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Submission declined on 29 December 2024 by WaddlesJP13 (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner. Declined by WaddlesJP13 4 months ago. | ![]() |
Submission declined on 29 December 2024 by SafariScribe (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner. Declined by SafariScribe 4 months ago. | ![]() |
Submission declined on 23 December 2024 by CSMention269 (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by CSMention269 4 months ago.
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Clinical and statistical prediction are two distinct methods used to integrate multiple data points for decision-making across various domains.[1][2] Clinical prediction relies on human judgment, expertise, and experience to mentally integrate information, while statistical prediction employs mathematical formulas, algorithms, or models to systematically combine quantitative data. These approaches emerged as formal concepts in the mid-20th century, though their underlying principles have been practiced much longer.
These prediction methods have applications across fields including medicine, psychology, criminal justice, business forecasting, personnel selection, and education. In practice, clinical prediction remains the dominant approach in most of these fields. For example, clinicians typically rely on their professional judgment when combining symptom data, test results, and patient history to reach diagnoses or treatment decisions, rather than using statistical formulas. Similarly, in criminal justice, while statistical tools exist for assessing recidivism risk, many consequential decisions about sentencing and parole continue to be made primarily through clinical judgment.
The comparative accuracy of these approaches has been extensively studied since the 1950s, with Paul Meehl's 1954 publication "Clinical versus Statistical Prediction" serving as a landmark contribution to this field.[3][4] Meta-analyses have consistently demonstrated that statistical methods typically match or exceed the accuracy of clinical prediction.[5]
Multiple studies have shown that even simple statistical models with equally weighted or randomly weighted variables often outperform expert clinical judgment in specific predictive tasks.[6][7] Despite this substantial body of evidence supporting the efficacy of statistical approaches, clinical prediction continues to dominate professional practice in many fields.
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