**Data Analysis- Overview**

Data analysis is one of the most important steps to draw conclusions from the analytical procedures. The ability to replicate measurements, while obtaining data is crucial for establishing a solid basis of comparison. Any uncertainty in the measurements is calculated in order to determine the variability or reliability of the measurements. These values of uncertainty allow for the interpretation of the precision of the collected data and guard against large error within the measurements taken. The recorded data is then collectively arranged to make sense of the information collected by methods including, but not limited to, graphs, charts, and tables. The data set can then be further interpreted by finding patterns, similarities, and differences which can be represented in a variety of ways, such as the use of a trend line. The collective data can be further studied using statistical analysis.

Various methods of statistical analysis allow for the comparisons of two or more sets of similar data. It is important to find the average value using the Gaussian distribution in order to obtain the true average. The standard deviation must be found in order to determine how closely a data point falls in relation to the average value. The F-test is used in order to compare standard deviation values of two data sets. This decides how comparable the data sets are with each other. The t-test is used to determine if two sets of data are statistically similar or different from one another. The Q-test is performed to identify and possibly remove questionable values from a given data set. Finally, a calibration curve is constructed to compare known standards of an analyte to unknown samples.

For the proper analysis of each water sample collected, it is important to note the details of where and when the sample was obtained. In analyzing the concentration of ions in the collected water samples, the method of ion chromatography was used. Ion chromatography measures height and area of a sample in microSiemens. A calibration curve from standard solutions is constructed using the results from ion chromatography versus the known concentration in parts per million. From this curve, data is extrapolated in order to determine the unknown concentrations in parts per million.

Content on this web page authored by Chibuokem Amuneke-Nze, Amanda Prie & Michael Devine