This book investigates the core question of why data science projects frequently fail or produce misleading results due to human error and cognitive bias. Author Gary Smith, a professor of economics, utilizes his academic background and extensive research into historical data failures to argue that technical proficiency is insufficient without rigorous logical scrutiny. He posits that the primary obstacles in the field are not computational, but rather the result of human tendencies toward lazy thinking, confirmation bias, and the misuse of statistical models.

Experts and practitioners frequently cite this work as a necessary cautionary text for those entering the field of data analytics. Readers often note that the prose is accessible and avoids overly dense jargon, making it a useful resource for both business leaders and data professionals.