Proper analysis of empirical data is arguably one of the most important tasks that both scientists and engineers are routinely asked to do. While a computer usually records and analyzes the data obtained from an experiment, it is ultimately up to people to program the computer and eventually interpret the results. Typical data collected from experiments usually contain a lot of inexact and noisy values which arise from both random and systematic errors. Random errors arise from natural limitations of making physical measurements whereas systematic errors arise from blunders in the measuring process. Either way, statistical methods of various levels of difficulty and sophistication must be used to properly extract information and interpret empirical results.