The authors critically examine Barabási's claim that human activities, particularly email communication, exhibit scale-free dynamics. They argue that the reported power-law distributions in time intervals between consecutive emails and reply delays are artifacts of the empirical data analysis and do not accurately reflect real-world behavior. The authors identify several significant flaws in Barabási's analysis, including the unrealistic short time intervals between emails and the incorrect interpretation of the data as a power-law distribution. Instead, they propose that a log-normal distribution better describes the data, as evidenced by Bayesian model selection analysis. The priority-queuing model, which Barabási uses to explain the "bursty" nature of human activity, is also criticized for its unrealistic predictions and lack of fit to the data. The authors conclude that their findings challenge the validity of Barabási's claims and suggest that a log-normal distribution is a more appropriate model for understanding human email communication patterns.The authors critically examine Barabási's claim that human activities, particularly email communication, exhibit scale-free dynamics. They argue that the reported power-law distributions in time intervals between consecutive emails and reply delays are artifacts of the empirical data analysis and do not accurately reflect real-world behavior. The authors identify several significant flaws in Barabási's analysis, including the unrealistic short time intervals between emails and the incorrect interpretation of the data as a power-law distribution. Instead, they propose that a log-normal distribution better describes the data, as evidenced by Bayesian model selection analysis. The priority-queuing model, which Barabási uses to explain the "bursty" nature of human activity, is also criticized for its unrealistic predictions and lack of fit to the data. The authors conclude that their findings challenge the validity of Barabási's claims and suggest that a log-normal distribution is a more appropriate model for understanding human email communication patterns.