Statistics

At this point, we need to consider the basics of data analysis in psychological research in more detail. In this chapter, we first focus on descriptive statistics—a set of techniques for summarizing and displaying the data from your sample. Then we address a set of techniques called inferential statistics. We focus, in particular, on null hypothesis testing, the most common approach to inferential statistics in psychological research, including a conceptual overview and a review of several null hypothesis testing techniques. Finally, look at some long-standing criticisms of inferential statistics and some ways of dealing with these criticisms.

**Note that this chapter has been adapted from the source material (Jhangiani, et al., 2019). In particular, we have merged chapter XII. Descriptive Statistics and chapter XIII. Inferential Statistics

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53. Describing Statistical Relationships

The text discusses statistical methods for analyzing differences between groups and the strength of relationships between variables. It highlights a study by Ollendick et al. (2009) comparing treatments for children's phobias, showing exposure therapy as more effective than education. The study uses means and standard deviations to describe differences and introduces Cohen’s d as a measure of effect size, interpreting values to determine the strength of differences. Additionally, it touches on correlations, using Pearson’s r to evaluate relationships, with guidelines for interpreting its values. Examples include studies on name order affecting response time and self-esteem correlations, emphasizing both linear and nonlinear relationships. The text also notes that effect size does not imply causality, stressing careful interpretation of results.