What This Document Is
These are lecture notes from Health Behavior Statistical Methods (HP 340) at the University of Southern California, specifically from a session covering introductory statistical hypothesis testing. The notes focus on the foundational concepts needed to understand and apply common statistical tests, including z-tests and t-tests. It builds upon prior material regarding estimation and sampling distributions, and serves as a core component of the course’s exploration of inferential statistics. The material aligns with content found in Kiess & Green Chapter 8.
Why This Document Matters
This resource is invaluable for students enrolled in health behavior, public health, or related fields requiring a strong understanding of statistical analysis. It’s particularly helpful for those preparing to design research studies, analyze data, and interpret findings. If you’re struggling to grasp the principles behind hypothesis testing, or need a clear overview of when to use parametric versus non-parametric tests, these notes can provide a solid foundation. They are best used *during* or *immediately after* a lecture on these topics to reinforce learning and clarify complex ideas.
Common Limitations or Challenges
These notes are a record of a single lecture and do not constitute a comprehensive textbook or self-contained learning module. They assume a baseline understanding of descriptive statistics, sampling distributions, and confidence intervals. The notes do not provide step-by-step calculations or detailed walkthroughs of statistical software applications. Access to the full document is required to see specific examples, flowcharts, and detailed explanations of the concepts presented.
What This Document Provides
* An introduction to the core principles of statistical hypothesis testing.
* A discussion of null and alternative hypotheses, including one- and two-sided approaches.
* An overview of the distinction between parametric and non-parametric statistical tests.
* A framework for understanding the role of statistical inference in research.
* A high-level overview of the steps involved in statistical decision-making.
* References to key concepts from Kiess & Green, providing context within the course curriculum.
