Gerd Gigerenzer and Ulrich Hoffrage explore the idea that the mind may not be inherently predisposed against performing Bayesian inference. They argue that the computational complexity of Bayesian algorithms is reduced when information is presented in frequency formats rather than probability formats. Frequency formats, which correspond to the sequential acquisition of information in natural sampling, are more intuitive and computationally simpler. The authors present two studies that test these predictions, showing that participants who were presented with information in frequency formats derived up to 50% of Bayesian inferences. They also discuss the limitations of previous research and propose a theoretical framework to explain why frequency formats improve Bayesian reasoning. The article concludes by suggesting that understanding and improving Bayesian reasoning can be achieved through engineering human cognitive processes rather than just observing errors.Gerd Gigerenzer and Ulrich Hoffrage explore the idea that the mind may not be inherently predisposed against performing Bayesian inference. They argue that the computational complexity of Bayesian algorithms is reduced when information is presented in frequency formats rather than probability formats. Frequency formats, which correspond to the sequential acquisition of information in natural sampling, are more intuitive and computationally simpler. The authors present two studies that test these predictions, showing that participants who were presented with information in frequency formats derived up to 50% of Bayesian inferences. They also discuss the limitations of previous research and propose a theoretical framework to explain why frequency formats improve Bayesian reasoning. The article concludes by suggesting that understanding and improving Bayesian reasoning can be achieved through engineering human cognitive processes rather than just observing errors.