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2: Unit 2- Hypothesis Testing

  • Page ID
    43789
    • Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin
    • University of Missouri System

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    In Unit 1, we learned the basics of statistics—what they are, how they work, and the mathematical and conceptual principles that guide them. In Unit 2, we will learn to use everything from the previous unit to test hypotheses, formal statements of research questions that form the backbone of statistical inference and scientific progress. This unit focuses on hypothesis tests about means, and Unit 3 will continue to use hypothesis testing for other types of data, statistics, and relationships.

    • 2.1: Introduction to Hypothesis Testing
      This page provides an overview of hypothesis testing, including the formulation of null (H0) and alternative hypotheses (H1), significance levels, and the statistical tools needed for evaluation, such as z tests and p-values. It details a four-step procedure for testing, including hypothesis formulation, critical value determination, test statistic calculation, and decision-making.
    • 2.2: Introduction to t Tests
      This page covers the transition from basic statistics to hypothesis testing using t-tests and confidence intervals. It introduces the t statistic, which varies with degrees of freedom, and outlines a hypothesis testing process, including critical value determination and calculation of test statistics. The chapter emphasizes the importance of understanding variability and presents multiple examples, including oil change times and public opinion on craft beer.
    • 2.3: Related Samples
      This page focuses on analyzing changes in a variable over time by calculating difference scores, essential for hypothesis testing. It discusses related samples and hypothesis formulation, emphasizing a shift toward evaluating change. Examples include a consultant finding significant job satisfaction improvements and a bank failing to show changes in public opinion.
    • 2.4: Independent Samples
      This page discusses independent samples t-tests, outlining the key concepts, calculations, and hypothesis testing procedures involved. It emphasizes the importance of pooled variance, standard error, effect size, and confidence intervals in data interpretation. Specific examples illustrate comparing moods after different movie genres, as well as differences in busyness between early birds and night owls.


    This page titled 2: Unit 2- Hypothesis Testing is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin via source content that was edited to the style and standards of the LibreTexts platform.