ABSTRACT
The data derived from quantitative research methods take the form of numbers, and therefore statistical analysis is used to explore patterns and relationships in those data. This chapter introduces key terms, concepts, and the logic behind how and why statistical tests are performed on aggregated data. Descriptive statistics and inferential statistics are defined and described, as are univariate, bivariate, and multivariate statistics. In quantitative research, evidence is used to determine whether data fit predictions. Those predictions are presented in the form of hypotheses, or, when there is not enough prior evidence or theoretical justification to make a prediction in the form of a hypothesis, the researcher uses research questions. This chapter guides the reader through hypothesis and research question formation and then, step by step, through the principles behind testing whether hypotheses are supported or refuted in light of the data obtained in the study. Statistical significance and probability levels are carefully defined in this chapter, and the ways to interpret significant and non-significant research findings are discussed. Type I and Type II errors are also defined and discussed as central components of Null Hypothesis Significance Testing. Although this chapter begins to introduce some of the mechanics involved in statistical analysis (which are continued in the next chapter, as well), the reader is reminded that behind those statistics there are important contexts, subjectivities, and experiences to keep firmly in mind.
