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Draft:Clinical Versus Statistical Prediction

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Clinical and statistical prediction are two distinct methods used to combine information for decision-making across various domains.[1][2] These approaches are employed when multiple data points need to be integrated to make informed decisions. For example, a medical professional may combine symptom information, test results, and patient history to arrive at a diagnosis, while a hiring manager might consider resumes, interview impressions, and test scores when selecting a candidate. Clinical prediction relies on human judgment to combine this information, whereas statistical prediction utilizes mathematical models and algorithms to do so. Although "clinical prediction" may imply medical decision-amking, its application extends to a wide range of areas, including in the prediction of criminal recidivism,[3] marital satisfaction, business failures, and magazine advertising sales.[4]

Comparing Clinical and Statistical Prediction

Clinical Prediction

Clinical prediction involves the subjective integration of information by thinking about it, where decision-makers rely on personal judgment, expertise, and experience rather than standardized methods or algorithms.[5] For example, a clinician might prioritize certain symptoms based on their experience. This approach is commonly used in fields like medicine, law, and personnel selection. Clinical prediction (or combination) is often also referred to as holistic, subjective, impressionistic, or informal.

Statistical Prediction

Statistical prediction involves the systematic combination of information using formulas or algorithms, requiring data to be quantified. Data sources are typically assigned weights, which can be determined through methods such as multiple regression, bootstrapped models (using Brunswik's lens model),[6] or equal weighting.[7] These weights are then mathematically integrated to make predictions. Statistical prediction (or combination) is often also referred to as actuarial, algorithmic, formal, or mechanical prediction.

Differences in Accuracy

Research indicates that statistical prediction is generally more accurate than clinical prediction.[8] This finding has been validated through meta-analyses across domains such as human health and behaviour,[9] mental health,[10] and admissions and hiring.[11] Notably, Paul Meehl’s 1954 work, Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, brought wide attention to this phenomenon within the social sciences.[12][13] Although not the first to note the differences between the two methods, Meehl’s work was pivotal in establishing a research programme dedicated to studying them. Despite its robust findings, however, the practical influence of the research programme has remained limited.

Key Findings

One notable discovery is the robustness of linear models in statistical prediction.[14] Studies have shown that even when predictor weights are assigned randomly, statistical methods can outperform clinical judgment, provided that positive predictors are weighted positively and negative predictors negatively. For instance, Robyn Dawes and Bernard Corrigan demonstrated in 1974 that random weights could surpass clinical prediction in accuracy.[15] This finding was later replicated in 2020 by Martin Yu and Nathan Kuncel in a study involving hiring assessments for management positions.[16][17]

Unit-weighting, where all predictors are equally weighted, has also been shown to be superior to clinical judgment in terms of decision accuracy. This approach assumes that predictor information is standardized to the same scale, for example a 5-point rating system. [18][19]

Limited Adoption in Practice

Despite evidence supporting the superior accuracy of statistical prediction, its adoption in practice remains limited.[20][21] This hesitancy is related to a phenomenon known as algorithm aversion, where individuals prefer human judgment over algorithmic methods despite the latter’s demonstrated effectiveness. For instance, in hiring processes, many organizations continue to rely heavily on clinical prediction rather than incorporating statistical methods, even when evidence suggests statistical approaches could improve hiring outcomes.[22] Factors contributing to this include lack of awareness, lack of experience in quantifying qualitative data, restriction of autonomy needs, and skepticism about the evidence.[23]

References

  1. ^ Grove, W. M., & Lloyd, M. (2006). Meehl’s contribution to clinical versus statistical prediction. Journal of Abnormal Psychology, 115(2), 192–194. https://doi.org/10.1037/0021-843X.115.2.192
  2. ^ Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
  3. ^ Wormith, J. S., Hogg, S., & Guzzo, L. (2012). The Predictive Validity of a General Risk/Needs Assessment Inventory on Sexual Offender Recidivism and an Exploration of the Professional Override. Criminal Justice and Behavior, 39(12), 1511–1538. https://doi.org/10.1177/0093854812455741
  4. ^ Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment, 12(1), 19–30. https://doi.org/10.1037/1040-3590.12.1.19
  5. ^ Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction. Psychology, Public Policy, and Law, 2(2), 293–323. https://psycnet.apa.org/doi/10.1037/1076-8971.2.2.293
  6. ^ Goldberg, L. R. (1970). Man versus model of man: A rationale, plus some evidence, for a method of improving on clinical inferences. Psychological Bulletin, 73(6), 422–432. https://psycnet.apa.org/doi/10.1037/h0029230
  7. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  8. ^ Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
  9. ^ Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment, 12(1), 19–30. https://doi.org/10.1037/1040-3590.12.1.19
  10. ^ Ægisdóttir, S., White, M. J., Spengler, P. M., Maugherman, A. S., Anderson, L. A., Cook, R. S., Nichols, C. N., Lampropoulos, G. K., Walker, B. S., Cohen, G., & Rush, J. D. (2006). The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction. The Counseling Psychologist, 34(3), 341–382. https://doi.org/10.1177/0011000005285875
  11. ^ Kuncel, N. R., Klieger, D. M., Connelly, B. S., & Ones, D. S. (2013). Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis. Journal of Applied Psychology, 98(6), 1060–1072. https://doi.org/10.1037/a0034156
  12. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  13. ^ L.A. Times Archives. (2003, February 20). Paul E. Meehl, 83; Psychologist Linked Schizophrenia to Genes. Los Angeles Times. https://www.latimes.com/archives/la-xpm-2003-feb-20-me-meehl20-story.html
  14. ^ Camerer, C. F., & Johnson, E. J. (1991). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 195–217). Cambridge University Press.
  15. ^ Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81(2), 95–106. https://doi.org/10.1037/h0037613
  16. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  17. ^ Yu, M., & Kuncel, N. (2020). Pushing the Limits for Judgmental Consistency: Comparing Random Weighting Schemes with Expert Judgments. Personnel Assessment and Decisions, 6(2). https://doi.org/10.25035/pad.2020.02.002
  18. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  19. ^ Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81(2), 95–106. https://doi.org/10.1037/h0037613
  20. ^ Lewis, M. (2017). The Undoing Project: A Friendship That Changed Our Minds. WW Norton.
  21. ^ Meehl, P. E. (1986). Causes and Effects of My Disturbing Little Book. Journal of Personality Assessment, 50(3), 370–375. https://doi.org/10.1207/s15327752jpa5003_6
  22. ^ Highhouse, S. (2008). Stubborn Reliance on Intuition and Subjectivity in Employee Selection. Industrial and Organizational Psychology, 1(3), 333–342. https://doi.org/10.1111/j.1754-9434.2008.00058.x
  23. ^ Neumann, M., Niessen, A. S. M., Hurks, P. P. M., & Meijer, R. R. (2023). Holistic and mechanical combination in psychological assessment: Why algorithms are underutilized and what is needed to increase their use. International Journal of Selection and Assessment, ijsa.12416. https://doi.org/10.1111/ijsa.12416
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