were counted as having been postponed until after the birthday celebration. There is no logical reason for this bizarre accounting other than it gave the results the authors wanted. Royer and Smith found that of the 26 famous people who died during their birth month, 13 died before their birthdays, 1 died on his birthday, and 12 died after their birthdays! The 26 people who died close to their birthdays were completely unsuccessful in postponing death. Royer and Smith also separated deaths into 30 days before the birthday, 30 days after, and so on, and found no evidence that people are able to postpone death. These discoveries led Smith to re-examine studies that allegedly showed people postponing death to celebrate Jewish celebrations and the Harvest Moon Festival. He found that these studies had also massaged the data to support the desired conclusion. Torturing Data Smith then turned to several other published studies that seemed implausible. Each illustrated Ronald Coase's dictum, “If you torture the data long enough, it will confess.” Smith's debunked claims that Asian-Americans are prone to have fatal hearts attacks on the fourth day of the month (the Baskerville effect), Chinese-Americans are more vulnerable to those diseases that Chinese astrology and traditional Chinese medicine associate with their birth years, people whose names have positive initials (such as ACE or VIP) live longer than do people with negative initials (such as PIG or DIE), people whose first names begin with the letter D die relatively young, baseball players who are elected to the Hall of Fame have a shortened life expectancy, baseball players born in August are prone to commit suicide, and hurricanes are more deadly if they have female name. Artificial Intelligence and Data Mining The scientific method is based on rigorous tests of falsifiable hypotheses. Data mining instead puts data before theory by searching for statistical patterns. Sometimes, theories are made up after a pattern is found (HARKing: Hypothesizing After the Results are Known); other times, it is assumed that theories are not needed. Artificial intelligence and machine learning systems, for example, often rely on data-mining algorithms to construct models with little or no human guidance. Smith argues that computers are not intelligent in any meaningful sense of the word, and should not be trusted to select models for making important decisions: “The real danger today is not that computers are smarter than us, but that we