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BackgroundBias in reasoning rather than knowledge gaps has been identified as the origin of most diagnostic errors. However, the role of knowledge in counteracting bias is unclear.ObjectiveTo examine whether knowledge of discriminating features (findings that discriminate between look-alike diseases) predicts susceptibility to bias.DesignThree-phase randomized experiment. Phase 1 (bias-inducing): Participants were exposed to a set of clinical cases (either hepatitis-IBD or AMI-encephalopathy). Phase 2 (diagnosis): All participants diagnosed the same cases; 4 resembled hepatitis-IBD, 4 AMI-encephalopathy (but all with different diagnoses). Availability bias was expected in the 4 cases similar to those encountered in phase 1. Phase 3 (knowledge evaluation): For each disease, participants decided (max. 2 s) which of 24 findings was associated with the disease. Accuracy of decisions on discriminating features, taken as a measure of knowledge, was expected to predict susceptibility to bias.ParticipantsInternal medicine residents at Erasmus MC, Netherlands.Main MeasuresThe frequency with which higher-knowledge and lower-knowledge physicians gave biased diagnoses based on phase 1 exposure (range 0–4). Time to diagnose was also measured.Key ResultsSixty-two physicians participated. Higher-knowledge physicians yielded to availability bias less often than lower-knowledge physicians (0.35 vs 0.97; p = 0.001; difference, 0.62 [95% CI, 0.28–0.95]). Whereas lower-knowledge physicians tended to make more of these errors on subjected-to-bias than on not-subjected-to-bias cases (p = 0.06; difference, 0.35 [CI, − 0.02–0.73]), higher-knowledge physicians resisted the bias (p = 0.28). Both groups spent more time to diagnose subjected-to-bias than not-subjected-to-bias cases (p = 0.04), without differences between groups.ConclusionsKnowledge of features that discriminate between look-alike diseases reduced susceptibility to bias in a simulated setting. Reflecting further may be required to overcome bias, but succeeding depends on having the appropriate knowledge. Future research should examine whether the findings apply to real practice and to more experienced physicians.

Purpose – This research consists of Behavioral Finance where it is focused on cognitive bias factors influence on Investment Decision with using the scope of research in three countries which are Indonesia, Thailand, and Japan.Design/methodology/approach – the method of research is categorized as quantitative research where it uses a questionnaire with 232 respondents. Then, the data is processed and analyzed using software SmartPLS 3.0.Findings – The findings reveal that overconfidence and availability bias have a significant positive effect on investment decisions, while herding behavior has a negative effect and anchoring bias shows no significant influence.Research limitations/implications – This research is limited by its relatively small sample size of 232 respondents across three culturally and economically diverse countries, which may affect the generalizability of the findings.Practical implications – The strong influence of overconfidence and availability bias highlights the need for improved investor education focused on risk awareness and critical analysis, especially in the digital era. Also, to prevent irrational behavior driven by herding, financial institutions and regulators should enhance collective financial literacy and promote transparent, data-driven decision-making.Originality/value – This result provides reasonable insight into why there is a difference in results between each country supported with the data and results from the previous research that have been done before.

Collective narcissism—a phenomenon in which individuals show excessively high regard for their own group—is ubiquitous in studies of small groups. We examined how Americans from the 50 U.S. states (N = 2,898) remembered U.S. history by asking them, “In terms of percentage, what do you think was your home state’s contribution to the history of the United States?” The mean state estimates ranged from 9% (Iowa) to 41% (Virginia), with the total contribution for all states equaling 907%, indicating strong collective narcissism. In comparison, ratings provided by nonresidents for states were much lower (but still high). Surprisingly, asking people questions about U.S. history before they made their judgment did not lower estimates. We argue that this ethnocentric bias is due to ego protection, selective memory retrieval processes involving the availability heuristic, and poor statistical reasoning. This study shows that biases that influence individual remembering also influence collective remembering.

Over-indebtedness in relation to consumer loans represents an important issue for consumers as it impacts their financial well-being. Identifying the risk factors associated with over-indebtedness is crucial in overcoming this problem. Existing literature shows that behavioral biases influence individuals' financial decision making. This study analyses the relationship between behavioral biases and over-indebtedness among consumer loan holders in Malaysia. It aims to investigate whether self-control bias, overconfidence, mental accounting, and availability bias are linked to over-indebtedness. The analysis is done based on a sample of 433 credit card or personal loan holders. The results indicate that self-control bias is linked to higher overall over-indebtedness. Meanwhile, overconfidence and mental accounting are linked to lower overall over-indebtedness. Availability bias is shown to worsen credit card debt repayment decisions. These findings highlight the need for financial education programs that address self-control issues and raise awareness of behavioral biases, helping consumers make more informed financial decisions. Additionally, policymakers in Malaysia can leverage these insights to design targeted strategies that reduce over-indebtedness in managing consumer loans. This study explores how behavioral biases like self-control, overconfidence, mental accounting and availability bias contribute to over-indebtedness among consumer loan holders in Malaysia. The findings suggest that targeted financial education can help consumers make better financial decisions, reduce debt, and improve overall financial well-being. Addressing these issues supports SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequalities) by promoting financial literacy, responsible borrowing, and economic stability.

Most research aiming at understanding the molecular foundations of life and disease has focused on a limited set of increasingly well-known proteins while the biological functions of many others remain poorly understood. We propose to form the Understudied Protein Initiative with the objective of reducing the annotation gap by systematically associating uncharacterized proteins with proteins of known function, thereby laying the groundwork for future detailed mechanistic studies.

The objective of the study was to assess trial-level factors associated with the contribution of individual participant data (IPD) to IPD meta-analyses, and to quantify the data availability bias, namely the difference between the effect estimates of trials contributing IPD and those not contributing IPD in the same systematic reviews (SRs). We included SRs of randomized controlled trials (RCTs) with IPD meta-analyses since 2011. We extracted trial-level characteristics and examined their association with IPD contribution. To assess the data availability bias, we retrieved odds ratios from the original RCT articles, calculated the ratio of odds ratios (RORs) between aggregate data (AD) meta-analyses of RCTs contributing IPD and those of RCTs not contributing IPD for each SR, and meta-analytically synthesized RORs. Of 728 eligible RCTs included in 31 SRs, 321 (44%) contributed IPD, whereas 407 (56%) did not. A recent publication year, larger number of participants, adequate allocation concealment, and impact factor ≥10 were associated with IPD contribution. We found the SRs yielded widely different estimates of RORs. Overall, there was no significant difference in the pooled effect estimates of AD meta-analyses between RCTs contributing and not contributing IPD (ROR 1.01, 95% confidence interval, 0.86–1.19). There was no consistent evidence of a data availability bias in recent IPD meta-analyses of RCTs with dichotomous outcomes. Higher methodological qualities of trials were associated with IPD contribution.

Single‐visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less‐than‐perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single‐visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida's east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species‐habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions. We developed an easily implemented single‐visit survey method to estimate the abundance of animals subject to both availability and detection bias. We show using simulations that the method returns reliable parameter estimates. We demonstrate application with eastern diamondback terrapins in Florida, USA, and we provide complete analysis details to allow use of the method for other applications.

Successful management strategies are important for conservation and allow accurate surveying and monitoring of populations for presence, abundance, and trend. This becomes challenging for cryptic, low-density species, and for animals that have complicated life histories where not every stage of the life cycle can be surveyed effectively. We used information from animal-borne data loggers to characterize the dive-surfacing behavior of cryptic loggerhead turtles (Caretta caretta) in the northwest Atlantic from 2009–2018. Our data covered a large geographic area off the east coast of North America, and allowed us to present estimates for and variation in 3 metrics that can be used to assess availability bias affecting visual surveys: average dive duration, average surface duration, and the proportion of time at the surface. We used a stochastic partial differential equation approach to construct spatiotemporal regression models for the availability bias metrics. Model predictions showed pronounced individual, spatial, and spatiotemporal (seasonal) variation among the 245 turtles. Overall, we estimated an average dive duration of 14.5 ± 1.36minutes (SE), an average surface duration of 15.1 ± 2.77minutes, and an average proportion of time at the surface of 0.50 (95% CI = 0.41–0.59). We made predictions of the 3 availability bias metrics on a 20-km × 20-km grid and further used predictions to explore seasonal variations. Our results contribute new insights into loggerhead turtle behavior and provide information that enables survey counts to be translated into more accurate abundance estimates.

In this study, professional engineers and designers (n = 30) participated in a 1-hour-long design activity in which they brainstormed a list of ideas for two design problems (a smart grill and a smart laundry machine), created a sketched concept for each design problem, filled out a survey about their perceptions of the market for the concept they developed, participated in a bias mitigation intervention and then repeated the pre-intervention steps. The design problems were intended to trigger availability bias based on the participants’ occupations (engineers and designers at a kitchen appliance company) as well as conflict between the gender of the participants and the gender-stereotyping of the household tasks fulfilled by the smart machines. Based on correlations in the market survey, the participants, who were mostly men, displayed availability bias toward the smart laundry machine design problem. A key marker of availability bias – an association between participants’ personal enjoyment of the product and the belief that the product would be commercially successful – was eliminated after the bias mitigation intervention. Qualitative analysis of participants’ reflections indicated that the intervention primarily assisted designers in making additional considerations for users, such as increasing accessibility and building awareness of excluded user groups.

Telework technologies have been known since the 1970s, yet their adoption levels remained low until Covid-19-related lockdowns and curfews. The known rational and non-rational technology acceptance theory and biases cannot fully explain this effect. One of the possible answers to fill this gap could be availability bias which has probably also affected the lag in adopting other technologies. To examine this phenomenon, we conducted a qualitative study with 22 interviews with individuals from different organizational backgrounds and telework adoption levels. Following a combination of inductive and deductive coding, we identified three key aspects of availability bias: intention, cognitive visibility, and cognitive transfer. The findings also allowed us to delineate this bias further from other biases, e.g., the status quo bias, and classical technology acceptance models, e.g., UTAUT. Thereby, this study examines a bias so far only very limitedly researched in the information systems and extends technology acceptance and cognitive bias literature. The findings should also enable practitioners to question their way of working and technology use more thoroughly.