Quantitative analysis is the chemistry of properly collecting and interpreting data. In a laboratory setting if you are lucky your data will lead to a clear answer to the question you are investigating. At times, however, you will find your data ambiguous or, more interestingly, that something unexpected is hidden within the data. This activity will guide you in exploring several important concepts in data analysis. By the end of this students should be able to: Objectives for Data Treatment Calculate mean and standard deviation from given sample data. Apply Grubbs test to given sample data for the identification of outliers. Given an experimental framework, identify and explain the types of calibration methods (curves, addition, and internal standard) used. Differentiate between calibration methods needed for the accurate measurement of an analyte in several different matrices. Calculate and analyze FOM for calibration methods Objectives for Result Reporting Carry out propagation of uncertainty in calculations and describe the type of experimental errors given sample data (systematic, random, gross). Calculate confidence interval and interpret the level of accuracy and precision of given sample data and expected value. Apply statistical tools, such as Student T’s and F tests to compare given sample data sets to identify different and/or equivalent results. Explain accuracy and precision between data sets from the application of statistical tools. Calculate and explain the meaning of limit of detection and limit of quantification for a given sample data set. Uncertainty Statistics