Jessica M. Utts, PhD Sources:
Jessica M. Utts is a statistician and Professor Emerita at the University of California, Irvine, whose work sits at the intersection of statistical methodology and the evaluation of evidence for anomalous phenomena. She served as President of the American Statistical Association in 2016 and has spent decades arguing that the same evidential standards applied to any other area of science should be applied to parapsychology, no more, and no less.[1] Her most cited contribution to parapsychology is a 1995 report commissioned by the U.S. government assessing two decades of remote viewing research, in which she concluded that the statistical evidence for anomalous cognition met conventional scientific standards. (Utts authored this government-commissioned evaluation; the same 1995 AIR review independently paired her assessment with a skeptical evaluation by Ray Hyman.)[2] She has also published widely on statistics education, meta-analysis methodology, and the misuse of p-values across the sciences.[3][4]
Career affiliations: Pennsylvania State University (MA and PhD, Statistics); University of California, Davis, Department of Statistics; University of California, Irvine, Department of Statistics (Chair, 2011–2016; Professor Emerita, 2018–present)[1]
Deeper dives — Utts:
Overview
Utts approaches parapsychology as a statistician, not as an experimenter. She does not run ESP experiments. What she does is ask whether the experiments others have run meet the same evidential standards that scientists apply everywhere else, and her answer, consistently, has been that they do, at least for certain well-controlled paradigms.[2][4] That position has made her an unusual figure: a mainstream statistician, elected president of the largest statistical professional society in the world, who has publicly concluded that the evidence for anomalous cognition is real by conventional scientific criteria.[1]
Her core methodological argument is that the field has been trapped by a misunderstanding of what replication means. When a study with small sample size fails to reach statistical significance, that is not evidence against the effect, it is evidence that the study was underpowered. The right question is whether the effect size is consistent across studies, not whether each study individually clears a p-value threshold.[5] This argument, which she developed in detail in a 1988 paper in the Journal of Parapsychology and expanded in her landmark 1991 Statistical Science review, has been widely cited both within parapsychology and in broader discussions of replication in science.[4][5]
Utts has also been a prominent voice in statistics education, arguing that introductory courses should teach students to be critical consumers of statistical claims rather than producers of calculations. Her textbooks Seeing Through Statistics and Mind On Statistics (co-authored with Robert Heckard) have been widely used in undergraduate courses.[1] Her 2003 paper in The American Statistician on what educated citizens should know about statistics has been reprinted and cited across multiple disciplines.[3]
Statistical Methodology and the Replication Debate
Utts’s 1988 paper in the Journal of Parapsychology traced the history of how “statistical significance” came to define “successful replication” in parapsychology, a convention she argued was both historically arbitrary and statistically incoherent. She showed that if the true hit rate in a ganzfeld-type experiment is modestly above chance, a study with a typical sample size of around 28 trials has very low power to detect it as statistically significant. The consequence is that most individual studies will appear to “fail” even when the effect is real and consistent. She advocated for effect sizes and confidence intervals as the primary tools for assessing replication, and for power calculations before studies are run.[5] Her 1991 Statistical Science paper extended this argument with a detailed review of meta-analyses across ganzfeld, forced-choice precognition, RNG, and dice experiments, concluding that the pattern of small but consistent nonzero effects across independent laboratories constituted an anomaly requiring explanation.[4] The paper attracted formal commentary from statisticians including Persi Diaconis, who remained skeptical, and Joel Greenhouse, who raised concerns about alternative explanations, exchanges that Utts addressed in a published rejoinder.[4]
Why This Is Hard to Study
Utts has been unusually explicit about the methodological vulnerabilities of the research she evaluates. The effects she describes are small, in the range social scientists call small to medium, which means individual studies are almost always underpowered. A single experiment with a typical sample size will usually not reach statistical significance even if the effect is real, and this creates a systematic appearance of non-replication that is actually an artifact of study design rather than evidence against the phenomenon.[5][2]
A second vulnerability is that the effects appear to be state-dependent and individually variable. The SRI and SAIC remote viewing data showed that a small fraction of participants, roughly one percent of those screened, produced consistently above-chance results, while unselected participants showed much smaller effects. This individual-difference structure means that aggregate results depend heavily on who is tested, and that replication attempts using unselected participants may systematically underestimate the phenomenon.[2]
A third challenge is that the phenomena resist mechanistic explanation. Utts has consistently noted that the absence of a known mechanism is not, by itself, evidence against an effect, the link between aspirin and reduced heart attack risk was established statistically long before the biochemical mechanism was understood, but she acknowledges that the lack of a theoretical framework makes it harder to design targeted experiments and easier for critics to dismiss positive results as artifacts.[7]
Effect Size, Power, and the Appearance of Non-Replication
Utts’s 1988 paper provided explicit power calculations for ganzfeld-type experiments. If the true hit rate is modestly above the chance baseline of 25%, a study with a typical sample size has very low power to detect the effect as statistically significant at the conventional threshold. This means that the majority of individual studies will appear to “fail” even when the underlying effect is consistent. When the “failed” studies are combined, the aggregate result is often statistically significant, a pattern Utts illustrated with the original ganzfeld database, where combining the trials from studies that individually failed to reach significance produced a statistically significant combined result.[5] She also showed that the autoganzfeld series at Psychophysical Research Laboratories, which was designed to address the methodological criticisms raised by Ray Hyman, produced effect sizes very close to those predicted by Rosenthal’s conservative adjustment of the earlier flawed database, a pattern she argued was inconsistent with the hypothesis that the earlier results were entirely due to methodological artifacts.[4]
Life and career
Utts completed her undergraduate degree in mathematics and psychology at the State University of New York at Binghamton, then earned both her MA and PhD in statistics at Pennsylvania State University, finishing her doctorate in 1978.[1] She joined the faculty at the University of California, Davis, where she spent the early and middle part of her career, and later moved to the University of California, Irvine, where she served as chair of the Department of Statistics from 2011 to 2016 and became Professor Emerita in 2018.[1]
Her involvement with parapsychology began in the mid-1980s as a statistical consultant to a classified U.S. government program testing psychic abilities.[8] That work led to her 1991 Statistical Science paper and eventually to her role as one of two expert reviewers, alongside skeptic Ray Hyman, commissioned by the American Institutes for Research to evaluate the government’s remote viewing program for the CIA and Congress in 1995.[9][2] The two reviewers agreed on the statistical analysis but disagreed on interpretation: Utts concluded the evidence met conventional scientific standards; Hyman concluded that non-psi explanations remained viable.[9]
Alongside her parapsychology work, Utts built a substantial career in statistics education and professional leadership. She served as Chief Reader for the Advanced Placement Statistics Exam, chaired the Committee of Presidents of Statistical Societies, and was elected President of the American Statistical Association for 2016.[1] Her ASA presidential address, published in the Journal of the American Statistical Association, argued for greater public visibility for the statistics profession and used examples from parapsychology to illustrate how anecdotal thinking can overwhelm statistical evidence in public discourse.[10] She has also served as Statistical Editor for both the Journal of the American Society for Psychical Research and the Journal of Parapsychology.[1]
Her contributions to statistics education have been recognized with the George Cobb Lifetime Achievement Award in Statistics Education and the Samuel S. Wilks Memorial Award from the American Statistical Association, among other honors.[1] The Society for Scientific Exploration awarded her the Dinsdale Award, and the Parapsychological Association gave her its Outstanding Contribution Award.[1]
The 1995 AIR/Stargate Evaluation
The American Institutes for Research evaluation was commissioned by the CIA at the direction of Congress as part of a decision about whether to continue the government’s remote viewing program, then known as Stargate. Utts and Hyman were brought in as the two expert scientific reviewers. Utts was given access to the full set of experiments conducted at SAIC under Edwin May‘s direction, as well as the earlier SRI database. Her report examined ten SAIC experiments in detail, assessed their methodological rigor against a checklist of controls for sensory leakage, randomization, and optional stopping, and compared the effect sizes to those from independent ganzfeld laboratories. She concluded that the effect sizes were consistent across SRI, SAIC, and the independent ganzfeld replications, and that this consistency was difficult to explain by methodological artifacts alone. She recommended that future research shift from proof-oriented to process-oriented work, asking how anomalous cognition works rather than whether it exists. Edwin May, the program’s principal investigator, later published a critical commentary arguing that the AIR evaluation had been structured to produce a negative conclusion about operational utility regardless of the research evidence, and that Utts had been explicitly instructed not to mention the NRC report or contact certain key program participants.[9] Utts confirmed in correspondence quoted by May that she had been told not to contact certain individuals and not to mention the NRC report in her review.[9]
Research
Utts’s research contributions to parapsychology fall into three main areas: meta-analytic reviews of the evidence base, methodological papers on how to evaluate and replicate psi experiments, and collaborative empirical work on specific phenomena.
Meta-analysis and the evidence base
Her 1991 Statistical Science paper remains her most cited contribution to the field. It surveyed four major meta-analyses, of ganzfeld experiments, forced-choice precognition, RNG studies, and dice experiments, and argued that the pattern of small but consistent effects across independent laboratories constituted a genuine anomaly. The paper also included a detailed account of the Hyman-Honorton ganzfeld debate, explaining why the vote-counting methods both sides used were statistically inadequate and why effect-size comparisons were the appropriate tool.[4] Her 1999 paper in the Journal of Scientific Exploration extended this analysis by placing the remote viewing and ganzfeld evidence alongside the antiplatelet/vascular disease literature, showing that the psi effect sizes were larger and the methodological controls comparable, a comparison designed to make the double standard in how the two bodies of evidence were treated visible.[7]
Ganzfeld and Remote Viewing Effect Sizes Across Laboratories
Utts’s 1995 government report compiled effect sizes from six independent sources: all remote viewing at SRI International (770 sessions), all remote viewing at SAIC (455 sessions), the Psychophysical Research Laboratories ganzfeld series in Princeton (329 sessions), the University of Amsterdam ganzfeld series (124 sessions), the University of Edinburgh ganzfeld series (97 sessions), and the Institute for Parapsychology in North Carolina (100 sessions). The hit rates across these six independent laboratories and two methodologically distinct paradigms ranged from 32% to 37% when 25% was expected by chance. Utts argued that this consistency across laboratories, experimenters, and methodologies was the key evidential point: if methodological artifacts were responsible, different artifacts would have to be operating in each laboratory to produce the same effect size.[2] The effect sizes for the remote viewing studies were computed using rank-order judging with five choices; the ganzfeld effect sizes used Cohen’s h based on direct hit rates with four choices. Both measures showed positive effects of similar magnitude. The combined data across all six sources showed a hit rate consistently around one in three when one in four was expected by chance.[7]
Methodological contributions
Utts has argued consistently that parapsychology’s “replication problem” is largely a statistical artifact. Her 1988 paper showed that the field’s convention of defining a successful replication as one that achieves statistical significance at a fixed threshold is incoherent when studies are underpowered, and that the same data that look like a failure to replicate when viewed as separate studies look like strong evidence for an effect when combined.[5] She has also written on the analysis of free-response data, on methods for combining p-values, and on the use of Bayesian methods as an alternative to classical hypothesis testing in parapsychology, arguing that Bayesian approaches are particularly appropriate because they allow prior beliefs to be made explicit and updated by data.[4][8]
Bayesian Analysis of Ganzfeld Data
Utts presented a Bayesian hierarchical analysis of 56 ganzfeld studies at a public lecture, using a combined dataset of 2,124 sessions with 709 direct hits. Under a non-informative prior, the posterior median hit rate was around 33% when 25% was expected by chance. Under a skeptic’s prior, centered on chance with very tight variance, the data shifted the posterior only slightly, to around 26%. Under an open-minded prior, the data drove the posterior to around 33%. The analysis illustrated why skeptics and believers looking at the same data reach different conclusions: the posterior is a function of both the prior and the data, and when the prior is very tight (as a committed skeptic’s would be), even large amounts of data produce only modest updating. The hierarchical model also revealed substantial study-to-study variation in hit rates, suggesting that a simple binomial model with a fixed hit rate is too simple and that moderating variables, participant characteristics, target type, experimenter effects, are likely important.[8]
Collaborative empirical work
Utts has collaborated on empirical studies in several areas. With Edwin May and S. J. P. Spottiswoode, she co-authored papers on Decision Augmentation Theory, a model proposing that anomalous cognition operates by biasing the selection of random events rather than directly influencing their outcomes, a framework that makes different predictions from a psychokinesis model and that Utts and May tested against the RNG database.[1] With Julia Mossbridge and Patrizio Tressoldi, she co-authored a 2012 meta-analysis of studies on predictive physiological anticipation, the finding that human physiology appears to differentiate between upcoming emotional and neutral stimuli before those stimuli are presented, and a 2014 follow-up review examining the phenomenon’s implications and potential applications.[6][11] With Stefan Schmidt and colleagues, she co-authored a 2004 meta-analysis of studies on distant intentionality and the sense of being stared at, published in the British Journal of Psychology.[12]
Predictive Anticipatory Activity Meta-Analysis
The 2012 Mossbridge, Tressoldi, and Utts meta-analysis examined 26 studies from seven independent laboratories published between 1978 and 2010. The studies used paradigms in which participants were exposed to randomly ordered emotional and neutral stimuli, and physiological measures, primarily electrodermal activity, but also heart rate, blood volume, pupil dilation, EEG, and fMRI, were recorded continuously. The meta-analysis tested a directional hypothesis: that the pre-stimulus physiological difference between conditions would have the same sign as the post-stimulus difference. Under both fixed-effect and random-effects models, the overall effect size was small but statistically significant. Higher-quality studies produced quantitatively larger effect sizes than lower-quality studies, and studies that performed expectation-bias analyses found that expectation bias could not account for the effects. The authors calculated that a very large number of contrary unpublished studies would be required to nullify the overall effect. The paper explicitly stated that the cause of the anticipatory activity lies within natural physical processes and that the phenomenon requires further investigation with agreed-upon protocols before strong conclusions can be drawn.[6]
Statistics education
Utts has argued that the way introductory statistics is taught has not kept pace with changes in the audience, the tools available, or the world students will encounter. Her 2003 paper in The American Statistician identified seven concepts, including the difference between statistical significance and practical importance, the problem of low power versus no effect, common sources of bias in surveys, and the confusion of conditional probabilities, that she argued every student who takes elementary statistics should understand.[3] She was a member of the ASA’s GAISE (Guidelines for Assessment and Instruction in Statistics Education) college group, which produced recommendations for modernizing introductory statistics instruction, and she has presented these recommendations at conferences in the United States, Japan, and elsewhere.[13][14][15]
GAISE Recommendations and Statistics Education Reform
The GAISE college report, to which Utts contributed as a member of the working group, made six core recommendations for introductory statistics instruction: emphasize statistical literacy and statistical thinking; use real data; stress conceptual understanding over procedural knowledge; foster active learning; use technology for developing understanding and analyzing data; and integrate assessments aligned with course goals. The report argued that the audience for introductory statistics had broadened substantially, most students would never conduct their own statistical analyses, and that the course should therefore prepare students to read and critically evaluate studies conducted by others. Utts’s own presentations on this topic used examples from parapsychology, medical research, and everyday news coverage to illustrate the seven concepts she identified as most commonly misunderstood.[13][14][15][3]
Key quantitative findings
A machine-readable summary of headline results from this researcher’s landmark papers, extracted from the source articles into ESP-Nexus’s structured study database. It reports what each study found; ESP-Nexus does not assess whether the effects are genuine.
| Paper | Reported finding | Effect / significance | Basis |
|---|---|---|---|
| Utts (1996), Journal of Scientific Exploration | Utts concludes that, using the standards applied to any other area of science, psychic functioning (anomalous cognition) has been well established: the statistical results are far beyond chance, methodological-flaw arguments are refuted, and effects of similar magnitude have been replicated across… | p = < 10^-20 | 154 studies; 227 participants; 26,000 trials |
Skeptical Critiques and Discussion
Critique 1: Ray Hyman, reviewing the same Stargate data as Utts, concluded that non-psi explanations remained viable and that the evidence did not establish the existence of a paranormal phenomenon.
Skeptic source: Hyman, R. (1996). Evaluation of a program on anomalous mental phenomena. Journal of Scientific Exploration, 10(1), 31–58. [Referenced in PND_e7096aa2 and P1999_afa06b78][9][7]
Response: Utts and Hyman agreed on the statistical analysis, both acknowledged that a statistically significant laboratory effect had been demonstrated. Their disagreement was interpretive: Hyman argued that the boundary conditions required to obtain significant results were not practical for intelligence operations and that non-psi explanations could not be ruled out, while Utts argued that the consistency of effect sizes across independent laboratories and methodologically distinct paradigms made artifact explanations implausible. Utts also noted that Hyman’s position required a different artifact explanation for each independent laboratory that had replicated the effect.[2][9][7]
Analysis. Both reviewers had access to the same data and agreed on the statistical facts; the dispute is about interpretation and the threshold for ruling out alternative explanations, not about the numbers themselves.
Critique 2: Persi Diaconis, commenting on Utts’s 1991 Statistical Science paper, argued that the autoganzfeld studies did not deserve serious scientific attention because they lacked active participation by qualified skeptics and magicians during the experiments.
Skeptic source: Diaconis, P. (1991). Comment. Statistical Science, 6, 385–386.[4]
Response: Utts’s rejoinder noted that the autoganzfeld experiments had in fact been examined by two professional mentalists, one of whom provided a written statement that the system offered excellent security against deception by subjects, and that Daryl Bem, a social psychologist and experienced mentalist, had visited the laboratory and participated as a subject. She also noted that the original publication followed the detailed reporting criteria established jointly by Hyman and Honorton, providing more methodological detail than earlier published records.[4]
Analysis. The autoganzfeld system was examined by mentalists and the protocol was designed with Hyman’s input, but Diaconis’s broader concern about the difficulty of detecting subtle fraud in published records remains a standing methodological challenge for the field.
Critique 3: Joel Greenhouse, commenting on Utts’s 1991 paper, argued that the experimental results were sensitive to alternative explanations, including experimenter expectancy effects, subjective judgment of hits, and publication bias, that Utts had not adequately addressed.
Skeptic source: Greenhouse, J. B. (1991). Comment: Parapsychology, on the margins of science. Statistical Science, 6, 386–389.[4]
Response: Utts’s rejoinder addressed each of Greenhouse’s four alternative explanations. On experimenter expectancy: Robert Rosenthal, the leading expert on experimenter effects, had reviewed the ganzfeld studies and concluded they were adequately controlled in this regard. On the definition of a direct hit: in the autoganzfeld procedure, the judge selects from four randomly chosen alternatives, so the probability of a hit by chance is 0.25 regardless of who does the judging or how the response is interpreted. On publication bias: the Parapsychological Association had maintained an official policy against selective reporting of positive results since 1975, and the autoganzfeld database had no file drawer. On the file-drawer estimate: even the more conservative Iyengar-Greenhouse method produced a file-drawer estimate that would require an implausibly large number of unreported studies given the size of the field.[4]
Analysis. The specific controls Utts cited address the most obvious versions of these alternative explanations, but the broader concern about subtle biases in a field with strong prior commitments on both sides remains unresolved.
Critique 4: Milton and Wiseman’s 1999 meta-analysis of post-1987 ganzfeld studies, published in Psychological Bulletin, found no significant overall effect, appearing to contradict the earlier positive findings that Utts had cited.
Skeptic source: Milton, J., & Wiseman, R. (1999). Does psi exist? Lack of replication of an anomalous process of information transfer. Psychological Bulletin, 125(4), 387–391.[16]
Response: Utts had noted in her 1999 paper that the Milton-Wiseman analysis suffered from methodological problems that had not been sufficiently resolved, including the inclusion of studies that deliberately deviated from the original autoganzfeld conditions to explore moderating variables, studies that were not designed as replications of the original paradigm. A separate analysis presented in the corpus showed that the Milton-Wiseman method of weighting studies equally rather than by sample size, and computing effect sizes as z/√n without sample size adjustment, produced a Stouffer z of 0.70 when the exact binomial analysis of the same data yielded a statistically significant result.[7][17]
Analysis. The disagreement turns on which studies should be included as replications of the original paradigm and which statistical method is appropriate; both sides have published analyses of the same data reaching different conclusions.
Influence and reception
Utts’s 1991 Statistical Science paper is among the most cited methodological contributions to parapsychology, and her 1995 government report is frequently cited as the most rigorous independent statistical assessment of the remote viewing evidence base.[18] Her argument that the same evidential standards should apply to parapsychology as to any other science has been influential in how the field presents itself to mainstream audiences, and her comparison of psi effect sizes to those in accepted medical research has been widely reproduced.[7]
Within the statistics profession, her work on statistics education has had broader reach than her parapsychology contributions. Her textbooks have been used in undergraduate courses at many institutions, and her GAISE contributions helped shape national recommendations for how introductory statistics should be taught.[13][1] Her 2003 American Statistician paper on statistical literacy has been reprinted in a research ethics textbook and cited in discussions of science communication across multiple fields.[1]
Her role in the 1995 Stargate evaluation gave her work unusual visibility outside academia. The report was released publicly and covered in mainstream media, making her one of the few statisticians whose conclusions about parapsychology reached a general audience.[19] Edwin May, the program’s principal investigator, later described her as the most knowledgeable of the reviewers about the project’s details, noting that she contacted him at least a dozen times to clarify points in the documents she was reading, in contrast to Hyman, who never called.[9]
Her 2022 paper in the Zeitschrift für Anomalistik on women in science used her own career trajectory, including her entry into parapsychology through statistical consulting, as a case study in how mentorship and supportive communities enable women to pursue unconventional research programs.[20]
Reception of the 1991 Statistical Science Paper
The 1991 Statistical Science paper was published with formal commentaries from five statisticians and parapsychologists: M. J. Bayarri and James Berger (who computed Bayes factors for the autoganzfeld data and found moderate evidence against the null for reasonable prior specifications); Ree Dawson (who discussed Bayesian hierarchical models for the ganzfeld data); Persi Diaconis (who remained skeptical but acknowledged the field was worth serious study); Joel Greenhouse (who raised alternative explanations); and Robert Morris (who discussed the implications for meta-analysis methodology). Utts published a detailed rejoinder addressing each commentator’s points. The exchange is notable as one of the few occasions on which a major statistics journal devoted substantial space to a rigorous methodological debate about parapsychological evidence.[4]
Open Questions: What Would Change the Picture
Utts has been explicit that she considers the proof-oriented phase of parapsychology research largely complete, there is, in her view, little more to be offered to anyone who does not already accept the current collection of data, and that the field should shift to process-oriented research asking how anomalous cognition works rather than whether it exists.[2] From that perspective, the open questions are mechanistic and applied rather than existential.
On the mechanistic side, the key unresolved question is what physical or psychological process underlies the consistent small-to-medium effects observed across paradigms. The SAIC experiments provided some suggestive evidence that performance correlates with the change in visual entropy of the target, consistent with a model in which a psychic sense, like the other senses, is a change detector, but this finding requires independent replication with pre-specified hypotheses.[2]
On the applied side, the question is whether the effect is large enough and reliable enough to be useful for any practical purpose. Utts noted that the effect size is in the range social scientists call small to medium, which means it is reliable enough to replicate in properly powered experiments but not large enough to be useful in any individual instance without a way to identify which responses are accurate.[2]
For skeptics, the key question is whether any preregistered, adversarially designed study, one in which both proponents and critics agree in advance on the protocol, the stopping rule, and the analysis, could produce results that would genuinely update their priors. Utts has noted that the moving target of requirements from organized skepticism has made it difficult to know what evidence would be accepted, and that the Hyman-Honorton Joint Communiqué was the first serious attempt to specify in advance what a convincing experiment would look like.[4]
Decision Augmentation Theory as a Testable Alternative
Utts co-authored papers with Edwin May and S. J. P. Spottiswoode developing Decision Augmentation Theory (DAT), which proposes that anomalous cognition operates by biasing the selection of random events, choosing which moment to press a button, which target to select, rather than by directly influencing the physical outcome of random processes. DAT makes different predictions from a psychokinesis model: it predicts that effect sizes should decrease as sample sizes increase (because larger samples are harder to bias through selection), and it predicts that the same effect should appear in both intentional and unintentional conditions. The authors tested these predictions against the RNG database and found results consistent with DAT. This framework is notable because it is a specific, testable mechanistic hypothesis that could in principle be falsified by a sufficiently large and well-designed experiment.[1]
Sources
- Utts, J. M. (n.d.). Curriculum vitae. University of California, Irvine, Department of Statistics. https://ics.uci.edu/~jutts/UttsCV.pdf R001 [Utts n.d.] ↩︎
- Utts, J. M. (1996). An Assessment of the Evidence for Psychic Functioning. Journal of Scientific Exploration. R002 [Utts 1996] ↩︎
- Utts, J. (2003). What educated citizens should know about statistics and probability. The American Statistician, 57(2), 74-79. https://doi.org/10.1198/0003130031630 R003 [Utts 2003] ↩︎
- Utts, J. (1991). Replication and meta-analysis in parapsychology. Statistical Science, 6(4), 363-378. https://doi.org/10.1214/ss/1177011577 R004 [Utts 1991] ↩︎
- Utts, J. (1988). Successful replication versus statistical significance. Journal of Parapsychology, 52(4), 305-320. R005 [Utts 1988] ↩︎
- Mossbridge, J., Tressoldi, P., & Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: A meta-analysis. Frontiers in Psychology, 3, 390. https://doi.org/10.3389/fpsyg.2012.00390 R006 [Mossbridge 2012] ↩︎
- Utts, J. M. (1999). The Significance of Statistics in Mind-Matter Research. Journal of Scientific Exploration. R007 [Utts 1999] ↩︎
- Utts, J., Norris, M., Suess, E., & Johnson, W. (2010). The strength of evidence versus the power of belief: Are we all Bayesians? In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS-8), Ljubljana, Slovenia. International Statistical Institute. https://iase-web.org/documents/papers/icots8/ICOTS8_8H1_UTTS.pdf R008 [Utts 2010] ↩︎
- May, E. C., Utts, J. M., & Spottiswoode, S. J. P. (1995). Decision augmentation theory: Toward a model of anomalous mental phenomena. Journal of Parapsychology, 59(3), 195-220. https://ics.uci.edu/~jutts/may.pdf R009 [May 1995] ↩︎
- Utts, J. (2016). Appreciating Statistics. Journal of the American Statistical Association, 111(516), 1373-1380. https://doi.org/10.1080/01621459.2016.1250592 R010 [Utts 2016] ↩︎
- Mossbridge, J. A., Tressoldi, P. E., & Utts, J. M. (2014). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis. Frontiers in Human Neuroscience. R011 [Mossbridge 2014] ↩︎
- Schmidt, S., Schneider, R., Utts, J., & Walach, H. (2004). Distant intentionality and the feeling of being stared at: Two meta-analyses. British Journal of Psychology, 95(2), 235-247. https://doi.org/10.1348/000712604773952449 R012 [Schmidt 2004] ↩︎
- Utts, J. (2008, December 13). [Conference presentation slides]. California Mathematics Council, Community Colleges (CMC3) South Conference, Monterey, CA. http://www.ics.uci.edu/~jutts/CMC3Utts2008.pdf R013 [Utts 2008] ↩︎
- Utts, J. (2006, September 12). [Conference presentation slides]. Japanese Behaviormetrics Society annual meeting, Tokyo, Japan. http://www.ics.uci.edu/~jutts/JapanBehaviormetrics.pdf R014 [Utts 2006] ↩︎
- Utts, J. (2006, September 7). [Conference presentation slides]. Japan Joint Statistics Meeting, Sendai, Japan. http://www.ics.uci.edu/~jutts/JapanJSM.pdf R015 [Utts 2006] ↩︎
- Milton, J., & Wiseman, R. (1999). Does psi exist? Lack of replication of an anomalous process of information transfer. Psychological Bulletin, 125(4), 387-391. https://doi.org/10.1037/0033-2909.125.4.387 R016 [Milton 1999] ↩︎
- Utts, J. (2007, July 15). Analysis of the Milton-Wiseman meta-analysis [Conference presentation slides]. Vancouver, British Columbia, Canada. http://www.ics.uci.edu/~jutts/MWAnalysis.pdf R017 [Utts 2007] ↩︎
- Katz, D. L., & Knowles, J. (2021). Associative remote viewing: The art and science of predicting outcomes for sports, politics, finances, and the lottery. Living Dreams Press. R018 [Katz 2021] ↩︎
- McMoneagle, J. (1993). Mind trek: Exploring consciousness, time, and space through remote viewing. Hampton Roads Publishing. R019 [McMoneagle 1993] ↩︎
- Utts, J. (2022). General and personal reflections on succeeding as a woman science researcher. Journal of Anomalistics, 22(2), 447-464. https://doi.org/10.23793/zfa.2022.447 R020 [Utts 2022] ↩︎
Deeper dives — Utts: