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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 instance, a medical professional combining symptom data, test results, and patient history to reach a diagnosis. Clinical prediction relies on human judgment to combine this information, whereas statistical prediction utilizes mathematical models and algorithms to do so. While early work on clinical prediction focused on mental health decisions, its application extends to other areas, including in the prediction of criminal recidivism,[3] marital satisfaction, business failures, and magazine advertising sales.[4]

Comparing Clinical and Statistical Prediction

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Clinical Prediction

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Clinical prediction involves the mental integration of information, 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

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Statistical prediction involves the systematic combination of information using formulas or algorithms, requiring data to be quantified. Data sources are often assigned weights, which can be derived through various statistical techniques such as multiple regression or other simpler methods such as unit-weighting.[6][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

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Research has generally found that statistical prediction tends to be more accurate than clinical prediction, though the degree of accuracy can vary across studies.[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] Paul Meehl's 1954 work, Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, helped to highlight 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.

Key Findings

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Research has shown the effectiveness of linear models in statistical prediction.[14] Research has indicated that statistical methods can outperform clinical judgment, even when predictor weights are assigned randomly, provided the predictors are correctly weighted. This finding has been observed in multiple studies.[15][16][17]

Another approach in statistical prediction, known as unit-weighting, has been shown to outperform clinical judgment in some contexts by assigning equal importance to all predictors (i.e., treating all predictors as having the same weight regardless of their individual relevance or significance).[18][19]

Limited Adoption of Statistical Prediction in Practice

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While statistical prediction has been found to be more accurate, its practical adoption remains limited in certain areas, possibly due to factors such as algorithm aversion, where people show a preference for human judgment despite the demonstrated effectiveness of algorithms.[20][21] 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 reluctance include a lack of awareness, limited experience in quantifying qualitative data, concerns about reduced autonomy, and skepticism regarding the evidence supporting statistical methods.[23]

References

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  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. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  7. ^ 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.
  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. ^ Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. William Collins.
  16. ^ Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81(2), 95–106. https://doi.org/10.1037/h0037613
  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. ^ 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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