The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction
Stefanía Ægisdóttir Michael J. White Paul M. Spengler
Alan S. Maugherman Linda A. Anderson
Robert S. Cook Cassandra N. Nichols
Georgios K. Lampropoulos Blain S. Walker Genna Cohen
Jeffrey D. Rush Ball State University
Clinical predictions made by mental health practitioners are compared with those using statistical approaches. Sixty-seven studies were identified from a comprehensive search of 56 years of research; 92 effect sizes were derived from these studies. The overall effect of clinical versus statistical prediction showed a somewhat greater accuracy for statisti- cal methods. The most stringent sample of studies, from which 48 effect sizes were extracted, indicated a 13% increase in accuracy using statistical versus clinical methods. Several variables influenced this overall effect. Clinical and statistical prediction accu- racy varied by type of prediction, the setting in which predictor data were gathered, the type of statistical formula used, and the amount of information available to the clinicians and the formulas. Recommendations are provided about when and under what conditions counseling psychologists might use statistical formulas as well as when they can rely on clinical methods. Implications for clinical judgment research and training are discussed.
A large portion of a counseling psychologist’s work involves deciding what information to collect about clients and, based on that information, predicting future client outcomes. This decision making can occur both at the microlevel, such as moment-to-moment decisions in a counseling session, and at the macrolevel, such as predictions about outcomes such as suicide risk, violence, and response to treatment (Spengler, Strohmer, Dixon, & Shivy, 1995). Because the quality of client care is often determined
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by the accuracy of these decisions (Dawes, Faust, & Meehl, 1989; Meyer et al., 1998; Spengler, 1998), determining the best means for decision making is important.
Two major approaches to decision making have been identified: the clin- ical and the statistical, which is also called mechanical (Dawes et al., 1989). Clinical prediction refers to any judgment using informal or intuitive processes to combine or integrate client data. Psychologists use the clinical method when their experience, interpersonal sensitivity, or theoretical perspective determines how they recall, synthesize, and interpret a client’s characteris- tics and circumstances.
Such intuitive or “gut-level” inferences are greatly reduced in the statis- tical approach. Predictions are based on empirically established relations between client data and the condition to be predicted (Dawes et al., 1989). A psychologist who declares that his or her clinical impression suggests a client may be suicidal has used the clinical method. By contrast, when using the statistical method, client data are entered into formulas, tables (e.g., actuarial tables), or charts that integrate client information with base rate and other empirical information to predict suicide risk. While the sta- tistical method is potentially 100% reproducible and well specified, the clinical method is neither as easily reproduced nor as clearly specified (Grove, Zald, Lebow, Snitz, & Nelson, 2000).
Meehl (1954) contended that while the clinical method requires specific
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Alan S. Maugherman is now in private practice at the Center for Psychological Development, Muncie, Indiana. Linda A. Anderson is now at the University Counseling and Psychological Services, Oregon State University. Robert S. Cook is now in private practice at Lifespan, North Logan, Utah. Cassandra N. Nichols is now at the Counseling and Testing Services, Washington State University. Blain S. Walker is now at the Tripler Army Medical Center, Honolulu, Hawaii. Genna R. Freels is now at the Louisiana Professional Academy, Lafayette, Louisiana. Funding for this project was provided to Paul M. Spengler by Grant MH56461 from the National Institute of Mental Health, Rockville, Maryland; by six grants to Paul M. Spengler from the Internal Grants Program for Faculty, Ball State University, Muncie, Indiana (two summer research grants, two summer graduate research assistant grants, an academic year research grant, and a new faculty research grant); and by three Lyell Bussell summer graduate research assistant grants, Ball State University, Muncie, Indiana. Preliminary findings from the clinical versus statistical prediction meta-analysis were presented at the annual meetings of the American Psychological Association in Washington, D.C., August 2000; Boston, August 1999; San Francisco, August 1998; Toronto, Ontario, Canada, August 1996; and New York City, August 1995, and by invited address at the annual meeting of the Society for Personality Assessment, New Orleans, March 1999. The authors extend special thanks to Kavita Ajmere, Corby Bubp, Jennifer Cleveland, Michelle Dorsey, Julie Eiser, Layla Hunton, Christine Look, Karsten Look, K. Christopher Rachal, Teresa Story, Marcus Whited, and Donald Winsted III for instrumental assistance in obtaining articles, coding studies, and managing data for the project. Correspondence concerning this article should be addressed to Stefanía Ægisdóttir, Department of Counseling Psychology, Teachers College 622, Ball State University, Muncie, IN 47306; e-mail: email@example.com.
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training, the statistical method does not. The statistical method requires only inserting data into a formula specifically designed for a particular judgment task. This may not be entirely true. Despite the use of formulas or tables to inte- grate information, the statistical method may require advanced training in the collection of relevant clinical and research-based information. Furthermore, advanced training may enhance a clinician’s perceptions, which in turn may be quantified and used in a statistical model. For example, a clinician may believe a client has the potential for suicide, translate this impression into a number on a rating scale, and then statistically combine this number with other data to predict the client’s risk for suicide (e.g., Westen & Weinberger, 2004).
To determine how counseling psychologists can be most effective in their decision making, knowing when and under what conditions each method is superior is important. The purpose of our meta-analysis is to articulate this knowledge.
THE CLINICAL VERSUS STATISTICAL PREDICTION CONTROVERSY
The search for the most accurate decision-making method is not new. In fact, this question has been debated for more than 60 years (Dawes et al., 1989; Meehl, 1954). The debate began with Meehl’s (1954) book Clinical Versus Statistical Prediction, in which Meehl theoretically analyzed the relation between the clinical and statistical methods of prediction and sum- marized findings from existing literature. Meehl found that in all but 1 of 20 studies, statistical methods were more accurate than or equally accurate as the clinical method. He concluded that clinicians’ time should be spent doing research and therapy, whereas work involving prognostic and classi- fication judgments should be left to statistical methods.
Holt (1958), the most adamant defender of the clinical method, criticized Meehl’s (1954) conclusions. Holt’s critique involved essentially two issues: (a) the identification and assessment of predictive variables and (b) how they should be integrated. Holt believed that Meehl had given insufficient attention to the sophistication with which clinicians identify the criteria they are predicting, what variables to use in their prediction, and the strength of the relationship between predictors and criteria. In Holt’s view, clinicians can identify these variables only through training and experience with com- parable cases. After identifying the relevant variables, they are assessed. Assessment may be as much qualitative as quantitative. Holt’s second criti- cism was that Meehl pitted “naïve clinical integration” of prediction against statistical decision making. A fairer comparison would compare statistical methods with “sophisticated clinical decision making and integration” (Holt,
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1958) According to Holt, sophisticated clinical decision making is based on sophisticated data. These data are both qualitative and quantitative, have been gathered in a systematic manner, and have known relationships with what is being predicted. Unlike the statistical approach, the clinician remains the prime instrument, combining the data and making predictions that are tailored to each person. Holt presented data suggesting a superiority for sophisticated clinical rather than statistical procedures in predicting suc- cess in clinical training. On the basis of these findings, Holt argued for a combination of clinical and statistical methods (i.e., sophisticated clinical) that would be systematic and controlled and sensitive to individual cases.
Since this time, other narrative and box-score reviews of the literature on the differential accuracy of clinical and statistical methods have been published (e.g., Dawes et al., 1989; Garb, 1994; Grove & Meehl, 1996; Kleinmuntz, 1990; Russell, 1995; Sawyer, 1966; Wiggins, 1981). Narrative reviews are traditional literature reviews; box-score reviews count statistical signifi- cance and summarize studies in a table format. These reviews nearly always supported Meehl’s (1954) conclusion that statistical methods were more accurate than or, at minimum, equally as accurate as clinical prediction methods (for a rare exception, see Russell, 1995). A recent meta-analysis of the clinical versus statistical literature (Grove et al., 2000) also supported earlier findings. Grove et al. (2000) found a consistent advantage (d = .12) for statistical prediction over clinical prediction across various types of nonmental health and mental health predictors and criteria.
Influence of the Statistical Versus Clinical Prediction Controversy
Despite the repeated conclusion that statistical prediction methods are more accurate than clinical procedures, the findings have had little influ- ence on clinical practice (Dawes et al., 1989; Meehl, 1986). Dawes et al. (1989) and Meehl (1986) offered several reasons for this. They suggested that clinicians lack familiarity with the literature on clinical versus statisti- cal prediction, are incredulous about the evidence, or believe that the com- parisons were procedurally biased in favor of statistical prediction methods. They also proposed that certain aspects of education, training, theoretical orientation, and values might influence their reluctance to recognize advan- tages associated with statistical decision methods. Most clinicians highly value interpersonal sensitivity. Because of this, some may believe that the use of predictive formulas dehumanizes their clients. A corollary is that the use of group-based statistics or nomothetic rules is inappropriate for any particular individual. Practitioners are also subject to confirmatory biases such that they recall instances in which their predictions were correct but fail
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to recall those instances in which statistical prediction was more accurate. One might add another reason: Some accounts have simply been too broad to convince mental health practitioners. In some instances (e.g., Grove et al., 2000), the literature that has reviewed clinical versus statistical prediction includes research and criteria that range from mental health to medicine to finance.
Use of Statistical Prediction Models
Perhaps as a result of the limited influence of clinical versus statistical comparison studies, few statistical prediction models are available to coun- seling psychologists and psychotherapy practitioners (Meyer et al., 1998). Clinicians working in forensic settings, however, have developed such models. In fact, numerous funded research projects have been conducted to aid in classifying juvenile and adult prison inmates (e.g., Gottfredson & Snyder, 2005; Quinsey, Harris, Rice, & Cormier, 1998; Steadman et al., 2000; Sullivan, Cirincione, Nelson, & Wallis, 2001). One such effort is the Violence Risk Appraisal Guide (VRAG; Quinsey et al., 1998), which is a statistical system for predicting recidivism of imprisoned violent offenders.
The VRAG is based on more than 600 Canadian maximum security inmates who were released either back to the community, to a minimum security hospital, or to a halfway house. After a series of correlation analy- ses of predictor and outcome variables, a set of stepwise regression models was conducted. These analyses reduced the original 50 predictors to 12. These include psychopathy checklist scores (Hare, 1991), elementary school mal- adjustment scores, presence of a personality disorder, age at time of offense, separation from parents at an age younger than 16, failure on prior condi- tional release, nonviolent offense history score (using an instrument), marital status, schizophrenia diagnosis, most serious injury of offender’s victim, alcohol abuse score, and gender of offender’s victim. Each predictor was assigned a specified weight based on the empirical relationship with the outcome variable. Summing the resultant scores yields a probability estimate for an offender’s future violence within the next 7 and 10 years. For instance, scores between +21 and +27 indicate a 76% likelihood for future violence, whereas scores between –21 and –15 suggest a probability of only 8%. The authors have validated this model for different groups of inmates (e.g., arsonists or sex offenders), with promising results (see Quinsey et al., 1998, for more detailed use of this statistical model).
In addition to forensics, statistical prediction formulas have been developed to aid with student selection for undergraduate, graduate, and profes- sional schools. As an example, Swets et al. (2000) described a statistical prediction formula used in selecting candidates at the University of Virginia
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School of Law. This formula consists of four predictor variables: undergradu- ate grade point average (GPA), mean GPA achieved by students from the appli- cants’ college, scores from the Law School Admissions Test (LSAT), and the mean LSAT score achieved by all students from the applicants’ college. Scores from these predictors are combined into a decision index of which a specific score indicates a threshold for admission. This statistical prediction formula predicts grades for 1st-year students and is used in combination with variables that are harder to quantify to select students (cf. Swets et al., 2000). Harvey- Cook and Taffler (2000) developed a statistical model using biographical data, frequently found on application forms and resumes, to predict success in accounting training in the United Kingdom. This six-variable model was devel- oped on 419 accounting trainees. Retesting it on an independent sample of 243 trainees, Harvey-Cook and Taffler showed that their model could classify 88% of those failing and 33% of those successful in accounting training. The authors concluded that their model delivered better and more cost-effective results than clinical judgment methods currently used for this purpose in the United Kingdom (Harvey-Cook & Taffler, 2000).
Test cutoff scores offer another instance of a statistical procedure that may aid clinical decision making. Indeed, cutoff scores may be more readily avail- able and easily constructed than statistical formulas. As an example, three Minnesota Multiphasic Personality Inventory–2 (MMPI-2) scales have been useful in classifying substance abuse: MacAndrew Alcoholism–Revised (MAC-R), Addiction Potential Scale (APS), and Addiction Acknowledgment Scale (AAS) (Rouse, Butcher, & Miller, 1999; Stein, Graham, Ben-Porath, & McNulty, 1999). Relying on data from 500 women and 333 men seeking outpatient mental health services, Stein et al. (1999) found that cutoff scores on the MAC-R correctly classified 86% of the women and 82% of the men as either substance abusers or nonabusers. In the case of the AAS, cutoff scores could predict 92% of women and 81% of men as either substance abusers or nonabusers. Likewise, cutoff scores with the APS enabled accurate prediction of 84% of women and 79% of men as either abusing or not abusing substances. This method of classification greatly exceeds the base rates for chance classi- fication. For women, the positive predictive power (ability to detect substance abusers) for MAC-R, AAS, and APS was 100%, 79%, and 53%, respec- tively. These values compare with a base rate of 16%. For men, the respec- tive positive predictive power for MAC-R, AAS, and APS was 100%, 68%, and 77%, respectively, which compare with a base rate of 27%.
Purpose of This Meta-Analysis
The current meta-analysis seeks to address several omissions in the liter- ature on clinical versus statistical prediction. Although Grove et al.’s (2000)
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important study confirmed prior conclusions about the relative merits of clinical and statistical prediction methods, questions still remain regarding the application of the findings to judgment tasks commonly encountered by mental health practitioners. First, their review combined literature from psy- chology, medicine, forensics, and finance. Consequently, conclusive results are not provided about prediction accuracy for mental health clinical and counseling practitioners relative to statistical methods. Second, even though Grove et al. examined the influence of various study design characteristics (e.g., type of criterion, professional background of clinical judges, judge’s level of experience, and amount of data available to the judges versus the statistical formulas), the influence of these design characteristics on the accuracy of prediction was not investigated when the criteria were psycho- logically related. Instead, Grove et al. investigated the influence of these study design variables on the overall effect, including studies from the diverse professional fields listed earlier. Similarly, despite Grove et al.’s examination of the influence of criterion type on the overall effect of the difference between clinical and statistical prediction accuracy, their criteria breakdown was broad (i.e., educational, financial, forensic, medical, clinical- personality, and other). The breakdown offers little specific information on which counseling psychologists can rely to decide when and under what conditions they should use clinical or statistical methods.
The first aim of this meta-analysis was to synthesize studies that had examined the differential accuracy of clinical and statistical judgments in which the prediction outcome was relevant to counseling psychology. Second, we examined studies in which predictions by mental health professionals were compared with statistical methods. In a typical study comparing these two methods, clinicians first synthesized client data (e.g., interview data, psychological tests, or a combination of interview information and one or more psychological tests) and then made a classification judgment (e.g., diagnosis) or predicted some future outcome (e.g., prognosis). The accuracy of these judgments was compared with a statistical prediction scheme in which the same (sometimes less or more) information was entered into a statistical formula that had been previously designed on the basis of empir- ical relations between the predictors (specific client data) and the criterion (the prediction task of interest). Third, we examined questions generated from the years of debate about the relative merits of clinical and statistical prediction. More specifically, we examined how the differential accuracy between clinical and statistical methods was affected by (a) type of predic- tion, (b) setting from which the data were gathered, (c) type of statistical formula, (d) amount of information provided to the clinician and formula, (e) information provided to the clinician about base rates, (f) clinician access to the statistical formula, (g) clinician expertness, (h) our evaluation
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of the validity of the criteria for accurate judgment, (i) publication source, (j) number of clinicians performing predictions in a study, (k) number of criterion behaviors predicted in a study, and (l) publication year.
Meta-analyses provide detailed and comprehensive syntheses of the professional literature. As such, they are especially relevant for bridging the gap between the science of counseling psychology and how it is prac- ticed by counseling psychologists (e.g., Chawalisz, 2003; Stricker, 2003; Wampold, 2003). The current meta-analysis addresses how counseling psychologists should best make decisions: when they should use clinical methods, when they would do well to use statistical methods, and when either is acceptable. In addition to relying on empirically supported treat- ment strategies, the counseling psychologist scientist-practitioner may be informed by the current meta-analysis about situations when statistical decision methods lead to more accurate clinical predictions than the clini- cal method.
Spengler et al. (1995), for instance, proposed an elaborated model of the scientist-practitioner, basing their clinical judgment model on Pepinsky and Pepinsky (1954). In this model, strategies were proposed to increase judgment accuracy relying on scientific reasoning. They suggested that to improve judgment accuracy, counseling psychologists (a) should be aware of their values, preferences, and expectations; (b) should use multiple methods of hypothesis testing (both confirming and disconfirming); and (c) should use judgment debiasing techniques (cf. Spengler et al., 1995). We argue that the current meta-analysis will further inform counseling psychologists as scientists not by providing information about the absolute accuracy of clinical judgment (i.e., when it may be most vulnerable to error) but instead by assessing the relative accuracy of clinical versus statistical prediction. Under conditions in which statistical prediction is superior, a successful debiasing method would use prediction methods based on empirical relations between variables (i.e., statistical methods). On the basis of this meta-analysis, we hope to also suggest options for future research and training relevant to decisions typically made by counseling psychologists.
This study is part of a large-scale meta-analysis of the clinical judgment (MACJ) literature (Spengler et al., 2005). By using 207 search terms, the MACJ project identified 1,135 published and unpublished studies between
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1970 and 1996 that met our criteria for inclusion in meta-analyses of mental health clinical judgment.1 However, because of the extensive historical debate about the relative benefits of statistical versus clinical prediction, we extended our search strategy for the present study back to 1940, thus defining the current study’s search period from 1940 to 1996. After an iterative process, we identified 156 studies that investigated some form of statistical predic- tion or model of clinical prediction for a mental health criterion compared with the accuracy of clinical judgment.
To be included in the meta-analysis, studies had to meet the following criteria: (a) a direct comparison was reported between predictions made by mental health practitioners (i.e., professionals or graduate students) and some statistical formula, (b) a psychological or a mental health prediction was made (e.g., diagnosis, prognosis, or psychological adjustment), (c) the clinicians and the statistical formula had access to the same predictor vari- ables or cues (even though the amount of information might vary), (d) the clinicians and the formula had to make the same predictions, and (e) the studies had to contain data sufficient to calculate effect sizes. By using these selection criteria, 67 studies qualified for inclusion, yielding 92 effect sizes. When Goldberg (1965) and Oskamp (1962) were included, 69 studies pro- duced 173 effect sizes (see below).
Specialized Coding Procedures
The MACJ project used a coding form with 122 categories or character- istics (see Spengler et al., 2005) that were grouped under the following con- ceptual categories: judgment task, judgment outcomes, stimulus material, clinician individual differences, standard for accuracy, method of study, and type of design. An additional coding form was constructed including study design characteristics identified in historical literature and more contempo- rary research as potentially affecting the differential accuracy of clinical and statistical prediction. These design characteristics became the indepen- dent variables. We also noted whether the statistical formulas were cross- validated. In this instance, cross-validated formulas refer to any statistical formulas that have been independently validated on a different sample from which the formula was originally derived. For example, if a score of 10 on an instrument developed to diagnose major depressive disorder correctly identifies 95% of persons with that disorder, to be considered a cross- validated formula (i.e., a score of 10 indicates major depression), that same score (10) had to be able to identify major depressive disorder with com- parable accuracy using another sample of persons with the disorder. Coding disagreements were resolved by discussion among coders until agreement was reached.
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Dependent Measure: Judgment Accuracy
The dependent variable for all analyses was judgment accuracy. For a study to be included, a criterion had to be established as the accurate judgment (e.g., prior diagnosis or arrest records). For instance, Goldberg (1970) compared clinical and statistical judgments of psychotic versus neurotic MMPI profiles to actual psychiatric diagnosis. MMPI profiles from psychiatric patients diagnosed as clearly psychotic or neurotic were presented to clinical psychologists. Their judgment about whether the MMPI profiles belonged to either a psychotic or a neurotic patient was compared with a statistical formula constructed to categorize patients as psychotic if five MMPI scales (the lie, 6 [Pa], 8 [Sc], 3 [Hy], 7 [Pt]) were elevated. These two types of judgments were compared with the prior diagnoses, which were considered the accurate judgment. In another example, Gardner, Lidz, Mulvay, and Shaw (1996) examined clinical and statistical prediction of future violence. Gardner et al. developed three sta- tistical formulas to predict future violence on the basis of clinical (e.g., diagnosis and drug use) and demographic information as well as informa- tion about prior violence. Violence prediction based on these three models was compared with predictions made by clinicians who had access to the same information as the formulas. The accuracy of these judgments was then compared with records of violent behavior (psychiatric, arrest, or commitment records) or from patients’ reports about their violent behav- ior. In this study, available records and patient self-reports about violent behavior served as the criteria for accurate judgment. Thus, specific crite- ria for accurate judgments had to be reported for a study to be included in this meta-analysis.
Effect Size Measure
As Cohen (1988) noted in his widely read book, effect sizes may be likened to the size of real differences between two groups. Estimates of effect size are thus estimates of population differences—they estimate what is really happening and are not distorted by sample size. The purpose of a meta-analysis is to estimate the effect size in a population of studies. In our case, a mean weighted effect size (d+) was used to represent the differ- ence between clinical and statistical prediction accuracy.2 Effect size mea- sured by d+ represents the mean difference between two samples of studies expressed in standard deviation units (g) and corrected for sample size (Johnson, 1993). More specifically, the mean judgment accuracy of statis- tical prediction was subtracted from the mean judgment accuracy of clinical
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prediction divided by the pooled standard deviation and then corrected for sample size.
In this study, the effect size (d+) represents the magnitude, not the statis- tical significance, of the relative difference between clinical and statistical prediction accuracy. A negative d+ value indicates superiority of the statis- tical prediction method, whereas a positive d+ indicates superiority of the clinical method. An effect of zero indicates exactly no difference between the two methods. In addition to d+, we reported the 95% confidence interval for the effect size. Confidence interval provides the same information as that extracted from significant tests. It permits one to say with 95% confidence (i.e., α = .05) that the true effect size falls within its boundaries. If the confi- dence interval includes zero, the population effect may be zero; one cannot say with confidence that a meaningful difference exists between the two groups. However, if the confidence interval does not include zero, one can conclude that a reliable difference exists between clinical and statistical prediction (e.g., Johnson, 1993).
The data were reduced to one representative effect size per study in most cases. This prevented bias that would result if a single study was overrepre- sented in the sample (Cooper, 1998; Rosenthal, 1991). For instance, if a study reported more than one statistical or clinical prediction (e.g., brain impair- ment and lateralization of the impairment; Adams, 1974), an average of the reported judgment accuracy statistic was calculated and transformed into one effect size. Also, if a study reported results from both non–cross-validated and cross-validated statistical prediction schemes, only results from the cross- validated statistical formula were used. This was done to prevent bias in favor of the statistical method, given the possibility of inflated correlations (based on spurious relations) between predictor and criterion variables in non–cross- validated statistical formulas (for more discussion of these issues, see Efron & Gong, 1983). Table 1 notes whether the studies used cross- or non–cross- validated statistical formulas.
Even though one average effect size per study was usually calculated, 18 studies produced more than one effect size (see Table 1). These studies included more than one design characteristic (independent variables) that we hypothesized might influence clinical versus statistical prediction accu- racy and reported accuracy statistics for various levels of the independent variable. An example would be a study investigating clinical versus statis- tical prediction under two conditions. In one condition, the clinicians have access to the statistical prediction scheme, whereas in another condition they do not. In our studies, we extracted two effect sizes. That is, the study’s two conditions (with and without access to the statistical formula) were treated as two independent projects. Furthermore, a study was allowed to produce
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