How to Perform and Interpret Chi-Square and T-Tests

For both statisticians and non-statisticians, knowing what data look like before more rigorous analyses is key to understanding what analyses can and should be performed. After all data have been cleaned up, descriptive statistics have been calculated and before more rigorous statistical analysis begins, it is a good idea to perform some basic inferential statistical tests such as chi-square and t-tests. This workshop concentrates on how to perform and interpret basic chi-square, and one-and two-sample t-tests. Additionally, how to plot your data using some of the statistical graphics options in SAS® 9.2 will be introduced. INTRODUCTION Millions of dollars each year are given to researchers to collect various types of data to aid in advancing science just a little more. Data is collected, entered, cleaned, and a statistician is told it is ready for analysis. When a statistician receives data, there are some basic statistical analyses that are performed first so that the statistician understands what the data look like. Statisticians examine distributions of categorical and continuous data to look for small frequency of occurrence, amount of missing data, skewness, variability and potential relationships. Not understanding what the data look like in their basic form can cause incorrect assumptions to be made and an incorrect statistical analysis could be performed later on. The first look at a data set includes plotting the data, determining appropriate descriptive statistics, and performing some basic inferential statistics like t-tests and chi-square tests. Knowing what descriptive statistic or inferential statistical analysis is appropriate for the type of variable or variables in the data, how to get SAS® to calculate the appropriate statistics, and what are the necessary things to report off the output is essential. SAS® has a whole host of statistical analysis tools for both descriptive and inferential statistical analyses.