New research from the University of Florida’s Warrington College of Business highlights a common cognitive bias called overprecision, where people tend to underestimate the variability in how long a service might take. This study, published in the journal Manufacturing & Service Operations Management, shows that this bias can affect consumer decisions—such as whether to join a queue or pay for a service—and has important implications for how businesses set prices and communicate wait times. The research suggests that in situations where customers cannot see the length of a line—referred to as "unobservable" settings—businesses may have the opportunity to charge higher prices than traditional models suggest. In these cases, the difference between what maximizes a company’s revenue and what benefits customers is significant, with customer benefit often being negative. This means that while businesses might gain more revenue, customers might end up waiting longer than expected without realizing it. When customers can see how long the line is—so-called "observable" settings—the optimal pricing can be either higher or lower than classical economic models predict. In these scenarios, consumers might benefit or suffer depending on how busy the system is. For example, during peak hours, customers might be better off knowing the line length, but during less busy times, the impact on their experience may be less clear. The study also suggests that businesses can increase revenue by sharing queue-length information during periods of extreme busyness or slow service. However, during times of moderate congestion, sharing this information may not be as beneficial, and managers must carefully consider the impact on both customer satisfaction and revenue. Interestingly, the research also notes that more information isn’t always better for consumers. In situations where the line is extremely long, revealing the queue length can sometimes reduce the benefit to customers, challenging the common belief that transparency always leads to better outcomes. The findings also offer practical insights, such as explaining why patients in healthcare systems often expect shorter waiting times than what they actually experience.