08 - Hypothesis testing (Oct. 26-Nov. 1)

Hypothesis Tests

Learning Objectives

  • Understand hypothesis testing as a type of statistical inference that uses sample data to make decisions about populations. 
  • Provide a detailed explanation of each of the listed steps in a hypothesis test:
    1. Determine the null and alternative hypotheses.
    2. Summarize the data into an appropriate test statistic.
    3. Find the p-value assuming the null hypothesis is true.
    4. Decide if the result is statistically significant based on the p-value.
    5. Report the conclusion in the context of the situation.
  • Explain the difference between a one-sided and two-sided hypothesis test. Give examples. 
  • Describe a type I and type II error and the role these errors play in interpreting results. 
  • Discuss the importance of power in planning and understanding research studies. 
  • Compare the t-distribution to the N(0,1) distribution. 
  • Identify a situation where a t statistic is more appropriate than using a z-score. 
  • Describe a one-sample t-test and how to compute the t statistic.

 Videos & Video Lectures

 

Readings

 

Assignments

 

Required Online Adobe Connect Sessions

  • Wednesday, Oct. 26, 7-8:20 pm ET - Happy Hour with Rod Little
    • Review Week 7, Homework Q&A, Preview Week 8, BIA Ryden
    • Week 8 Happy Hour recording
  • Monday, Oct. 31, 7-8:20 pm ET - Lab Connect meeting with Swastina Shrestha
    • Week 8 Lab Connect recording
    • Office Hour recording

Powerpoint Slides

 

Additional Resources