This paper presents three examples of nonparametric statistical tests that can aid certain decision-making processes in the community college by using basic student data to separate "scientific" decision-making from "opinion" decision-making. A brief example of each test in a decision-making situation is related to illustrate the utility of the test. The McNemar Test for Significance Change is presented in order to examine certain enrollment changes. The Kruskal-Wallis Test examines the population distribution of several independent samples of mathematics placement scores as they relate to the high schools that the students attended. A Chi-Square Analysis is used to test the independence of students' curriculum choices and high schools attended. These tests are recommended to the beginning community college researcher because they are easily computed and explained, do not require samples drawn from a normally distributed population, and are easily understood both in terms of theory and application and thus are less likely to be used inappropriately than parametric statistical tests. A brief list of elementary statistical texts is included with the suggestion that the researcher review them when utilizing nonparametric statistics in the generation of decision-making information. (Author/JDS)