"While the human brain is powerful tool for quickly recognizing patterns in data, it will frequently make errors in interpreting random data. Luckily, these mistakes occur in systematic and predictable ways. Statistical models provide an analytical framework that helps us avoid these error-prone heuristics and draw accurate conclusions from random data. This non-technical presentation highlights some tricks of the trade learned by studying data and the way the human brain processes. First, we introduce statistics as the science of data, and discuss how the popular conception of randomness differs from its technical definition. Later sections highlight the human brain as
a pattern recognition machine. Examples from published literature and media highlight systematic and predicable errors in human cognition as well as how poor data analysis and graphical displays can cause critical errors in analysis. Finally, we’ll talk about using statistical models for analysis, including how violations of model
assumptions should effect our analyses. "