1. Introduction
In the modern era, where science and technology continue to advance at an unprecedented pace, fostering scientific literacy is essential to meet the demands of the digital age. STEM education, which integrates science, technology, engineering, and mathematics, has become increasingly important as a key means of equipping individuals with the necessary skills to navigate this landscape. However, an increasing interest in science does not always translate into a deeper understanding of scientific principles. In some cases, it may coincide with an increase in pseudoscientific beliefs, which may appear scientific but lack empirical support and contradict established scientific knowledge (Timur et al., 2022).
Pseudoscientific beliefs are widespread and often rooted in cognitive biases that persist even among university students (Torres et al., 2020). Although these beliefs resemble scientific claims, they lack empirical validation and may carry significant societal consequences. From the spread of misinformation about vaccines to the popularity of pseudotherapies and miracle diets, pseudoscience influences public perception and decision making in critical areas such as health, environmental policies, and technology. With digital platforms accelerating the spread of information, the ability to distinguish scientific evidence from pseudoscientific claims has become more critical than ever, making scientific literacy a fundamental skill for informed decision making.
Pseudoscience has been defined in multiple ways, often emphasizing its superficial resemblance to genuine science while lacking its methodological rigor. According to Bunge (2014), pseudoscience presents non-scientific or non-technological claims as genuinely scientific or technological. Similarly, Preece and Baxter (2000) define pseudoscience as a collection of ideas or theories that, while presented as scientific, contradict standard scientific knowledge and cannot be tested or have repeatedly failed empirical validation. Fasce and Picó (2019) refine this definition by identifying four key characteristics that distinguish pseudoscience from general paranormal beliefs: It refers to an entity or process outside the domain of science. It relies on a deficient, non-scientific methodology. It lacks scientific evidence. It is presented as science.
Fasce argues that although the first three conditions may occur independently, the fourth must always be present for a claim to be considered pseudoscientific.
Previous studies indicate that gender differences may play a role in pseudoscientific endorsement, with some research suggesting that female students tend to score higher on pseudoscience scales compared to their male counterparts (Aarnio & Linderman, 2005; Huete-Pérez et al., 2022; McLeish, 1984; Preece & Baxter, 2000; Wilson, 2018). While the reasons for this discrepancy remain debated, some hypotheses include differences in sociocultural backgrounds, psychological aspects, epistemological beliefs, and educational experiences.
Additionally, the role of academic discipline has been increasingly recognized as a relevant factor in pseudoscience acceptance. Studies have found that business, humanities, and arts students may exhibit higher pseudoscientific beliefs than those in STEM disciplines (Huete-Pérez et al., 2022). These findings raise important questions about the effectiveness of science education across different academic contexts and highlight the need for discipline-specific interventions.
Over time, numerous instruments have been developed to assess pseudoscientific beliefs. However, many early scales, such as those proposed by Johnson and Pigliucci (2004), Lundström and Jakobsson (2009), and Majima et al. (2022), did not strictly distinguish between pseudoscience and paranormal beliefs. Fasce and Picó (2019) addressed this gap by designing a scale with strong conceptual foundations, focusing exclusively on pseudoscience. Building on their demarcation criteria and experimental findings from Blanco et al. (2015) and Griffiths et al. (2019), Torres et al. (2020) introduced the Pseudoscience Endorsement Scale (PES), a 20-item instrument in Spanish.
The Pseudoscience Endorsement Scale, developed by Torres et al. (2020), assesses the degree to which individuals accept pseudoscientific statements. Investigating PES scores in university students allows a deeper understanding of how pseudoscientific beliefs are distributed across different academic backgrounds and genders, providing valuable insights for intervention strategies.
This study employs the PES to assess the prevalence of pseudoscientific beliefs among university students from diverse academic disciplines, examining how these beliefs vary by gender and faculty. This research aims to inform educational interventions that strengthen scientific literacy and promote critical thinking by identifying patterns in pseudoscientific endorsement. Furthermore, these findings contribute to OECD Goal 4, which advocates for inclusive, equitable, and quality education while fostering lifelong learning and scientific reasoning (OECD, 2021; UNESCO, 2017).
Understanding how individual differences and educational contexts shape pseudoscientific beliefs is essential for developing effective science communication strategies. This study seeks to provide empirical data that can guide curriculum design and public policy efforts to reduce pseudoscientific thinking in higher education.
Aim and Structure of the Study
This study aims to investigate university students’ pseudoscientific beliefs, focusing on how they vary by gender and faculty. By examining these variables, the study aims to provide insights into the factors shaping belief systems within higher education.
Section 1 presents the Introduction, describing the theoretical and contextual background and emphasizing the relevance of pseudoscientific beliefs in the academic context. Section 2, Materials and Methods, describes the study design, demographics of participants, and instruments used, including the PES and the statistical methods applied. Section 3, Results, presents the findings, organized into gender and faculty analyses, highlighting significant differences in PES scores. The discussion interprets these results in Section 4 in the context of the existing literature. Finally, the conclusions in Section 5 summarize the main findings and provide recommendations for future research and educational strategies.
2. Materials and Methods
2.1. Research Design
The present observational study explores students’ responses to the PES among students from a private university in northeastern Mexico. The research questions were: (RQ. 1) Does gender play a significant role in participants’ levels of adherence to pseudoscientific beliefs as measured by the PES? (RQ. 2) Is there a significant difference in pseudoscience adherence levels between students from different faculties measured by the PES?
2.2. Participants
The sample included 794 students from six faculties offered at the selected university, as shown in Table 1: Art, Architecture, and Design (AAYD), Health Sciences (CS), Law and Social Sciences (DYCS), EYH (Education and Humanities), Engineering and Technologies (IYT), and Business (N). The distribution of students across these faculties was as follows: AAYD, 173 students (21.8%); CS, 138 students (17.4%); DYCS, 46 students (5.8%); EYH, 71 students (8.9%); IYT, 139 students (17.5%); and N, 227 students (28.6%). Of the participants, 492 (62%) identified as female, and 302 (38%) as male.
2.3. Questionnaires
The Pseudoscience Endorsement Scale, developed by Torres et al. (2020), assesses participants’ acceptance of pseudoscientific statements using a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). The scale consists of 20 items, each evaluating beliefs in pseudoscientific claims, such as “Listening to classical music, such as Mozart, makes children more intelligent”.
The full set of items includes: Radiation derived from using a mobile phone increases the risk of a brain tumor. A positive and optimistic attitude towards life helps to prevent cancer. We can learn languages listening to audios while we are asleep. Osteopathy is capable of causing the body to heal itself by manipulating muscles and bones. The manipulation of energies bringing hands close to the patient can cure physical and psychological maladies. Homeopathic remedies are effective as complements in treating some diseases. Stress is the principal cause of stomach ulcers. Natural remedies, such as Bach flower remedies, help overcome emotional imbalances. Using superficial insertion of needles in specific parts of the body, one can treat problems with pain. Nutritional supplements like vitamins or minerals can improve the state of one’s health and prevent diseases. Neuro-linguistic programming is effective in curing mental disorders and the improvement of quality of life in general. Using hypnosis, it is possible to discover hidden childhood traumas. One’s personality can be evaluated by studying the form of one’s handwriting. The application of magnetic fields on the body can be used to treat physical and emotional alterations. Listening to classical music, such as Mozart, makes children more intelligent. Our dreams can reflect unconscious desires. Exposure to Wi-Fi signals can cause symptoms such as frequent headaches, problems sleeping, or tiredness. The polygraph or lie detector is a valid method for detecting if someone is lying. Diets or detox therapies effectively eliminate toxic substances from the organism. It is possible to control others’ behaviour by means of subliminal messages.
The questionnaire was administered in Spanish, as initially developed and validated by the authors. Internal consistency was assessed using Cronbach’s alpha (). The PES demonstrated high reliability ( = 0.88), indicating strong internal consistency.
2.4. Application Procedure
During the fall semester of 2024, 794 students at a private university in northeastern Mexico completed the questionnaires as part of their Scientific and Technological Thinking course. The students completed the surveys in class under the supervision of their instructors. Participation was voluntary, and informed consent was obtained before the study. The anonymity of the responses was maintained throughout the research process.
2.5. Statistical Analysis and Data Processing
Data were analyzed using R (version 4.4.1), a statistical computing software. To evaluate whether there were significant differences in pseudoscientific beliefs (PES) scores by gender and faculty, the Kruskal–Wallis test was applied. This non-parametric test was selected to analyze ordinal and non-normally distributed data using Cohen’s d to measure the effect size. Additionally, pairwise comparisons between faculties were conducted using the Wilcoxon rank sum test to calculate p-values and identify specific group differences. These methods provided robust statistical insights into the relationships between variables.
3. Results
The results are presented in two main sections to address the research questions. First, the gender analysis evaluates differences in Pseudoscientific Belief Scale (PES) scores between male and female participants, responding to Research Question 1. Second, the career analysis examines variations in PES scores among the six academic faculties included in the study, addressing Research Question 2.
3.1. Gender Analysis
The gender analysis aimed to evaluate whether significant differences exist in Pseudoscientific Belief Scale (PES) scores between male and female participants. The Kruskal–Wallis test revealed statistically significant differences () and small effect size (−0.261), as shown in Table 2.
Based on these findings, the following subsections provide a detailed analysis of PES scores by gender.
(RQ1) PES by Gender
To further explore the differences in pseudoscientific beliefs (PES) between genders, Table 3 presents the mean and standard deviation (SD) of PES scores for male and female participants. These statistics summarize the central tendency and variability in scores by gender.
The analysis revealed a difference in the mean PES scores between genders, with female participants scoring higher (4.30 ± 0.948) than male participants (4.05 ± 1.01). These results indicate that, on average, female participants demonstrate a higher level of adherence to pseudoscientific beliefs than their male counterparts.
To visually represent these findings, Figure 1 displays the mean PES scores with error bars denoting the standard deviation for each gender.
The graph highlights the observed differences and reinforces the statistical results.
The findings confirm that gender plays a significant role in adherence to pseudoscientific beliefs, as measured by the PES. Female participants exhibited higher mean PES scores than males, indicating a greater susceptibility to pseudoscientific beliefs among women. This directly addresses Research Question 1, demonstrating a statistically significant difference in PES scores based on gender.
3.2. Faculty Analysis
The faculty analysis aimed to explore differences in Pseudoscientific Belief Scale scores among students from six academic faculties. The Kruskal–Wallis test revealed statistically significant differences in PES scores (), as shown in Table 4.
Given the significant differences in PES scores, pairwise comparisons using the Wilcoxon rank sum test were conducted to identify which faculties differed significantly. The following subsections detail the results for PES scores by faculty.
Given the significant differences in PES assessment, pairwise comparisons were performed using the Wilcoxon rank sum test to identify which specialties differed significantly. Although the CRT test does not show significant differences in the Kruskal–Wallis test, the Wilcoxon test was included analogously to indicate the difference between specialties. The following subsections detail the results of the PES and CRT evaluations by specialty.
(RQ2) PES by Faculty
Table 5 displays the mean PES scores across faculties, highlighting Business (N) as the group with the highest mean score () and Engineering and Technologies (IYT) with the lowest mean score ().
Pairwise comparisons using the Wilcoxon test revealed several significant differences in PES scores between faculties, as summarized in Table 6.
Pairwise comparisons using the Wilcoxon rank sum test revealed several significant differences in PES scores between specific faculties. These results highlight variations in adherence to pseudoscientific beliefs across academic disciplines: Students from Art, Architecture, and Design scored significantly higher than students from Health Sciences (p = 0.006), Education and the Humanities (p = 0.047), and Engineering and Technologies (p = 0.0000937). Students from Business (N) showed significantly higher PES scores compared to students from Health Sciences (p = 0.001), Education and the Humanities (p = 0.02), and Engineering and Technologies (p = 0.00002).
These findings suggest that students from faculties such as Business and Art, Architecture, and Design show a greater tendency to endorse pseudoscientific beliefs than those with a stronger emphasis on scientific or technical disciplines, such as Engineering and Technologies.
To further illustrate these differences, Figure 2 provides a visual representation of the mean PES scores by faculty, including error bars to denote the variability within each group.
The results show that faculty significantly affects adherence to pseudoscientific beliefs. Students from Business (N) and Art, Architecture, and Design (AAYD) had the highest PES scores, while those from Engineering and Technologies (IYT) had the lowest. These findings address Research Question 3, confirming that pseudoscientific beliefs vary across academic disciplines.
4. Discussion
This study aimed to assess pseudoscientific belief endorsement among university students, focusing on gender differences and academic discipline. The findings align with prior research on pseudoscientific beliefs.
4.1. Gender Differences in Pseudoscientific Beliefs
Our results indicate that female students scored significantly higher on the PES than male students. This trend has also been reported in the latest national survey on the perception of science in Mexico. This survey reveals that 81% of women aged 18 to 29 agree that there are adequate means for treating diseases that science does not recognize, such as acupuncture, chiropractic, homeopathy, and spiritual cleansings. In contrast, 62% of men in the same age group think the same (INEGI, 2017).
These results are consistent with other studies conducted in the United States, Canada and the UK. For instance, McLeish (1984) found that Canadian girls were slightly more superstitious than boys, while British students showed no significant gender differences. Similarly, Preece and Baxter (2000) reported that females were generally less skeptical than males, except for beliefs about extraterrestrial visits. Aarnio and Linderman (2005) suggested that higher intuitiveness and lower analytical thinking among females partially explained their more significant endorsement of pseudoscientific beliefs. Wilson found that, before a critical thinking intervention, females exhibited higher belief levels than males (mean = 11.1%, p = 0.006). However, after completing the critical thinking course, belief levels dropped significantly, with females showing more significant reductions than males in beliefs about witchcraft (p = 0.03) and precognition (p = 0.003) (Wilson, 2018).
Table 7 summarizes the key instruments used to measure pseudoscientific beliefs with gender differences.
What could be the causes of these gender differences? Beyond cognitive styles, recent studies, as shown in Table 8, suggest that moral reasoning, emotions, and health-related factors may also influence pseudoscientific beliefs. Čavojová et al. (2024) found that pseudoscientific beliefs and an external locus of control predicted positive attitudes toward Complementary and Alternative Medicine (CAM), particularly among women with cancer. Putri et al. (2024) reported that males tend to have higher self-confidence in physics and chemistry, which may contribute to differences in scientific skepticism. Benito-Boillos et al. (2022) found a positive correlation between negative emotions in the classroom and pseudoscientific belief endorsement, suggesting that affective factors play a role in belief persistence. Similarly, Piejka and Okruszek (2020) demonstrated that moral inclinations strongly predict adherence to pseudoscientific beliefs, indicating that ethical reasoning may influence belief formation. The perception of science could be contributing to this difference. The national survey on the perception of science reveals that 59% of young women aged 18 to 29 believe technological development leads to an artificial and dehumanized way of living, compared to 49% of men in the same age group (INEGI, 2017).
4.2. Faculty Differences in Pseudoscientific Beliefs
Our findings also reveal a clear pattern of PES endorsement across academic disciplines:
Business > Architecture & Design > Law & Social Sciences > Education & Humanities ≈ Health Sciences > Engineering Technology
This pattern suggests that students in STEM fields tend to be more skeptical of pseudoscience than those in business, humanities, and social sciences. Table 9 presents the main instruments that have been used to measure these differences in academic disciplines.
Our study aligns with the study conducted in Spain by Huete-Pérez et al. (2022), who consistently found that individuals in Science and Technology, Engineering, and Architecture had the lowest levels of Epistemically Unwarranted Belief (EUB) endorsement (p < 0.001). In contrast, students in Social and Legal Sciences had the highest levels, with Arts and Humanities and Health Sciences falling in between. These results also align with Torres et al. (2020), who reported a negative correlation between PES scores and science literacy (p < 0.001), reinforcing the idea that scientific training plays a protective role against pseudoscientific thinking.
What could be the causes of these differences? The relationship between academic background and pseudoscience acceptance may also be influenced by epistemic cognition and exposure to scientific reasoning. Walker et al. (2002) did not find a direct correlation between science knowledge and paranormal beliefs, but Torres et al. (2023) demonstrated that bullshit receptivity was the strongest predictor of pseudoscience endorsement. Similarly, Zaboski and Therriault (2020) found that marketing strategies significantly influence belief persistence, suggesting that how pseudoscience is framed and presented may shape its perceived credibility.
In addition to these cognitive factors, the studies summarized in Table 10 suggest that ideological and socio-cognitive profiles shape belief formation. Lobato and Holbrook (2024) found that pseudoscience and conspiracy beliefs were associated with pessimistic socio-cognitive profiles, low credibility in science, and high social dominance orientation (SDO). Martínez et al. (2024) reported that pseudoscientific beliefs are associated with the tendency to generate false memories, highlighting the role of motivated reasoning in belief reinforcement. Finally, Bromme et al. (2008) argued that academic training significantly impacts epistemological beliefs, with STEM students developing more substantial skepticism toward pseudoscience than those in humanities or social sciences.
4.3. In General
Our findings suggest that educational interventions should take gender and specialty differences into account. The gender gap in pseudoscientific belief can be attributed to various cognitive factors (van Elk, 2019), attitudes toward alternative medicine (Čavojová et al., 2024), and gender perceptions of careers (Putri et al., 2024). Some studies suggest that women, on average, exhibit higher levels of magical beliefs that may make them more receptive to pseudoscientific ideas (Čavojová et al., 2024; Ward & King, 2020).
Similarly, business students tend to rely more on intuition, persuasion, and heuristic decision making, making them more susceptible to pseudoscientific concepts like the law of attraction or personality-based management techniques. Unlike science students trained in critical thinking and the scientific method, business students often prioritize practical outcomes over empirical validation. Additionally, the ability to distinguish between scientific language and bullshit may play a key role in this phenomenon (Torres et al., 2023; Walker et al., 2002).
A science-based pedagogy that accounts for gender and disciplinary differences should prioritize strengthening critical thinking while adapting to diverse learning styles and cognitive profiles. For women, who often exhibit higher magical thinking (Moral de la Rubia, 2011; Ward & King, 2020), the approach should integrate emotional relevance, skepticism without dismissing intuition, and increased visibility of female scientists to build trust in science. For business students, the emphasis should be on statistical reasoning, debunking popular business myths, and analyzing real cases of fraud and pseudoscience in the corporate world. Meanwhile, science students should be trained in effective science communication and decision making under uncertainty. In all disciplines, approaches such as case-based learning, problem-solving activities, and real-world applications can enhance the relevance and engagement of scientific reasoning. The goal is not just to teach science but to make it meaningful and accessible, reducing susceptibility to pseudoscience in various sectors of society.
4.4. Limitations of the Study
This study was conducted among undergraduate students from Generation Z (Centennials) within a private university setting, with an upper-middle socioeconomic background and extensive access to technological resources. Although these factors offer valuable insights into pseudoscientific beliefs among young adults in privileged academic settings, they also limit the generalizability of the findings to populations with different socioeconomic or academic backgrounds.
Additionally, idiosyncratic aspects rooted in customs and traditions may contribute to endorsing pseudoscience, fostering openness to mystical and alternative explanations, ranging from herbal medicine to spiritual cleansings (Moral de la Rubia, 2011). Further research is needed in this area. Ultimately, this study could serve as a stepping stone for expansion across Latin America due to cultural similarities and the comparable educational lag in mathematics and science (OECD, 2023).
5. Conclusions
This study provides empirical evidence on gender differences and variations across academic disciplines in accepting pseudoscientific beliefs among college students. The findings reinforce the previous literature, which shows that female students tend to score higher on beliefs related to pseudoscience. In contrast, students in STEM fields show more skepticism than those in social sciences, business, and humanities.
The results align with previous research indicating that women are generally less skeptical toward pseudoscience. These differences could be attributed to variations in epistemic cognition, cognitive styles, and emotional influences. Recent studies suggest that a greater reliance on intuitive thinking, a more significant external locus of control, and different perceptions of science may contribute to these differences.
However, evidence also highlights that critical thinking interventions can reduce pseudoscientific beliefs among both genders, with some studies reporting significative reductions among female participants. This suggests that educational interventions designed to improve analytical reasoning and scientific literacy could be effective strategies to mitigate pseudoscientific thinking across genders (Wilson, 2018).
Moreover, students in STEM (engineering, natural sciences, and technology) areas showed the lowest endorsement of pseudoscientific beliefs, while students in business, social sciences, and humanities showed significantly higher scores. This is consistent with previous findings suggesting that formal scientific training strengthens skepticism and reduces epistemically unjustified beliefs (Huete-Pérez et al., 2022).
This trend can be explained by differences in exposure to scientific methodology, emphasis on evidence-based reasoning, and training in analytical thinking across disciplines. The presence of causal illusion and susceptibility to misinformation appears more prevalent in non-STEM disciplines, reinforcing the need for interdisciplinary approaches to scientific literacy education.
The findings of this study underscore the importance of integrating scientific reasoning and critical thinking development into university curricula. It is essential to conduct interventions with interdisciplinary approaches that incorporate scientific literacy in social sciences, humanities, and business programs, as well as to help students critically evaluate information sources and differentiate between science and pseudoscience, and to generate gender-sensitive materials that recognize and address differences in belief formation.
The Mexican context fosters belief in pseudosciences due to a combination of educational and sociocultural factors. According to the latest Program for International Student Assessment, Mexican pre-university students are experiencing an evident decline in mathematical and scientific skills (OECD, 2023). Therefore, the lack of emphasis on critical thinking makes it easier for pseudoscientific ideas to spread, whereas the belief that science dehumanizes pushes people to seek alternative solutions (INEGI, 2017).
Pseudoscientific beliefs persist even among highly educated populations, but scientific literacy and analytical reasoning training can be crucial in mitigating their influence. Tackling these beliefs through education, interdisciplinary strategies, and evidence-based interventions is critical to foster a more scientifically literate society capable of distinguishing reliable knowledge from misinformation.
Conceptualization, J.A.A., W.X.C.-G., A.S.-G., and O.A.-G.; methodology, J.A.A., W.X.C.-G., A.S.-G., H.M.-H., and O.A.-G.; software, J.A.A., W.X.C.-G., and O.A.-G.; validation, J.A.A., W.X.C.-G., A.S.-G., H.M.-H., and O.A.-G.; formal analysis, J.A.A., W.X.C.-G., and O.A.-G.; investigation, J.A.A., W.X.C.-G., A.S.-G., H.M.-H., and O.A.-G.; resources, J.A.A., W.X.C.-G., and O.A.-G.; data curation, J.A.A., W.X.C.-G., and O.A.-G.; writing—original draft preparation, J.A.A., W.X.C.-G., A.S.-G., H.M.-H., and O.A.-G.; writing—review and editing, J.A.A., W.X.C.-G., A.S.-G., H.M.-H., and O.A.-G.; visualization, J.A.A., W.X.C.-G., and O.A.-G.; supervision, A.S.-G., H.M.-H., and O.A.-G.; project administration, J.A.A., W.X.C.-G., and O.A-G. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study due to the nature of the research, as it involved only voluntary participation in a questionnaire administered to students, with no sensitive personal data collected.
Informed consent was obtained from all participants involved in the study. Students participated voluntarily, and it was explicitly communicated that by completing the questionnaire, they were consenting to participate.
Data are contained within the article.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Mean PES scores and standard deviations by gender. It compares the mean scores on the Pseudoscience Endorsement Scale between male and female students, with error bars representing standard deviations.
Figure 2 Mean PES scores and standard deviations by academic faculty, illustrating the differences in pseudoscientific belief endorsement across faculties. Error bars represent the standard deviations.
Distribution of participants across six faculties by gender. This table summarizes the number and percentage of male and female students from each academic faculty who participated in the study.
| Male | Female | Total | |
|---|---|---|---|
| AAYD | 25 (3.1%) | 148 (18.6%) | 173 (21.8%) |
| CS | 28 (3.5%) | 110 (13.9%) | 138 (17.4%) |
| DYCS | 22 (2.8%) | 24 (3.0%) | 46 (5.8%) |
| EYH | 14 (1.8%) | 57 (7.2%) | 71 (8.9%) |
| IYT | 91 (11.5%) | 48 (6.0%) | 139 (17.5%) |
| N | 122 (15.4%) | 105 (13.2%) | 227 (28.6%) |
| Total | 302 (38.0%) | 492 (62.0%) | 794 (100.0%) |
Results of Kruskal–Wallis test comparing PES scores by gender. The table reports the test statistic (K), degrees of freedom (DF), p-value, and effect size (Effsize) indicating statistically significant gender differences.
| K | DF | p | Effsize | |
|---|---|---|---|---|
| PES | 16.8 | 1 | 0.0000416 * | −0.261 (small) |
* p < 0.05.
Descriptive statistics for PES scores by gender. It presents the mean and standard deviation (SD) of Pseudoscientific Belief Scale scores for male and female participants (n).
| Gender | n | Mean | SD |
|---|---|---|---|
| Male | 302 | 4.05 | 1.01 |
| Female | 492 | 4.30 | 0.948 |
Results of Kruskal–Wallis test comparing PES scores by faculty. The analysis reveals statistically significant differences in pseudoscientific beliefs across academic disciplines.
| K | DF | p | Effsize | |
|---|---|---|---|---|
| PES | 29.0 | 5 | 0.0000231 * | 0.0305 (small) |
* p < 0.05.
Descriptive statistics for PES scores across academic faculties. The table displays each faculty’s number of participants (n), mean scores, and standard deviations (SD).
| Faculty | n | Mean | SD |
|---|---|---|---|
| AAYD | 173 | 4.35 | 0.89 |
| CS | 138 | 4.06 | 0.93 |
| DYSC | 46 | 4.21 | 0.90 |
| EYH | 71 | 4.07 | 0.49 |
| IYT | 139 | 3.92 | 1.04 |
| N | 227 | 4.41 | 1.00 |
Pairwise Wilcoxon rank sum test results for PES scores across faculties. The table summarizes the p-values and effect sizes for comparisons between each pair of academic disciplines.
| AAYD | CS | DYCS | EYH | IYT | N | |
|---|---|---|---|---|---|---|
| p -Value (Effsize) | p -Value (Effsize) | p -Value (Effsize) | p -Value (Effsize) | p -Value (Effsize) | p -Value (Effsize) | |
| AAYD | - | 0.0606 | 3888.5 | 6289.5 | 13,365 | 16,711 |
| CS | - | - | 2897 | 4842.5 | 10,453.5 | 12,557 |
| DYCS | - | - | - | 1736 | 3767 | 4542 |
| EYH | - | - | - | - | 5367.5 | 6590.5 |
| IYT | - | - | - | - | - | 11,587.5 |
| N | - | - | - | - | - | - |
Summary of key instruments assessing pseudoscientific beliefs and observed gender differences. The table highlights previous tools used in empirical studies and their primary findings.
| Authors | Instrument | Context | Main Findings |
|---|---|---|---|
| PBS | USA—Undergrad | Females higher in religion and precognition (p < 0.001) | |
| Superstition Test | UK and Canada—Children | Girls more superstitious than boys | |
| Exeter Superstitions Q. | UK—High School | Females less skeptical than males (p < 0.001) | |
| Modified PBS | USA—Undergrad | Females > Males before CT course (p = 0.006) |
Recent empirical studies examining gender differences in pseudoscientific belief endorsement.
| Authors | Sample | Main Findings |
|---|---|---|
| 193 (University Students) | Moral inclinations predict pseudoscientific beliefs | |
| 125 (High School Students) | Pseudoscience correlated with negative emotions in classroom | |
| 200 (Jr. High School Students) | Males have higher science perception | |
| 177 (Women with Cancer) | External locus of control → CAM endorsement |
Key instruments and findings from recent studies measuring pseudoscientific beliefs across academic disciplines.
| Authors | Instrument | Context | Main Findings |
|---|---|---|---|
| PSBI | Sweden—High School | Students that attend theoretical alignments tend to express lower confidence in the pseudo-scientific statements (p < 0.05) | |
| PES | Spain—Psychology | PES positively correlated with causal illusion (p < 0.001) | |
| PSEUDO-R | Spain—General Pop. | Pseudoscience correlated with Paranormal and Conspiracy beliefs | |
| PEUBI | Spain—General Pop. | Females presented higher level of EUB endorsement than males |
Recent research exploring academic background and its relationship to pseudoscientific beliefs.
| Authors | Sample | Main Findings |
|---|---|---|
| 98 (General Pop) | Pseudoscientific beliefs positively correlated with illusion of causality | |
| 435 (General Pop.) | Identified 4 belief dimensions: Conservative, Cover-ups, Magical, Pseudoscience | |
| 170 (General Pop.) | Pseudoscientific beliefs associated with false | |
| 411 (General Pop.) | Pseudoscience correlated with low credibility in science and high social dominance |
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; Chavarría-Garza, Wendy Xiomara 2
; Aquines-Gutiérrez Osvaldo 3
; Santos-Guevara Ayax 3
; Martínez-Huerta, Humberto 3
1 Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico; [email protected] (J.A.A.); [email protected] (W.X.C.-G.); [email protected] (A.S.-G.); [email protected] (H.M.-H.), School of Education and Humanities, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
2 Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico; [email protected] (J.A.A.); [email protected] (W.X.C.-G.); [email protected] (A.S.-G.); [email protected] (H.M.-H.), Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, Avenida Universidad S/N, Ciudad Universitaria, San Nicolás de los Garza 66455, NL, Mexico
3 Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico; [email protected] (J.A.A.); [email protected] (W.X.C.-G.); [email protected] (A.S.-G.); [email protected] (H.M.-H.)