This chapter explores key concepts of inferential statistics, essential for drawing conclusions from data and making inferences. It explains the purpose and significance of inferential statistics in research, covering foundational concepts such as random sampling, probability distributions, and the central limit theorem, which are critical tools for statistical inference. The chapter also guides you through point and interval estimation, with a focus on calculating confidence intervals and understanding the differences between one-tailed and two-tailed intervals. Additionally, the chapter discusses hypothesis testing, explaining the difference between one-tailed and two-tailed tests, along with the concepts of Type I and Type II errors. Practical advice is provided on minimizing these errors to enhance the accuracy of statistical inferences. Examples throughout the chapter illustrate these concepts, making them more accessible and easier to apply.

Chapter 6 introduces the hypothesis-testing process and relevance of standard error in reaching statistical conclusions about whether to accept or reject the null hypothesis using the z-test statistic. Type I and Type II errors, along with the types of statistical tests researchers apply in testing hypotheses, are presented; these include one-tailed (directional) versus two-tailed (nondirectional) tests. Three important decision rules are the sampling distribution of means, the level of significance, and critical regions. Type I and Type II errors influence the decisions we make about our predictions of relationships between variables. Statistical decision-making is never error-free, but we have some control in reducing these types of errors.