About This Chapter
Hypothesis Testing in Statistics - Chapter Summary
These lessons outline statistical hypothesis testing, including its applications in testing large samples or testing for a mean, and some examples of common hypothesis testing errors. You can print out our text lessons or lesson transcripts for offline study. As you're watching the video lessons, use the video tabs feature to go back or skip ahead to review certain sections. Feel free to contact an instructor if you have questions about the topics covered. Once you complete this chapter, you should understand the following:
- What the difference between one-tailed and two-tailed tests is
- How to use t-tests to assess statistical differences in groups
- What the similarities between t-tests and z-tests are
- How to use hypothesis testing for proportions and for a difference between two proportions
- How hypothesis testing applies to matched pairs
1. What is Hypothesis Testing? - Definition, Steps & Examples
A proper hypothesis test consists of four steps. After watching this video lesson, you'll understand how to create a hypothesis test to help you confirm or disprove an assumption.
2. Hypothesis Testing Large Independent Samples
In this lesson, you're going to learn about hypothesis testing of large independent samples. You'll learn about the assumptions in such a hypothesis test, and we'll work through an example together.
3. Conducting Hypothesis Testing for a Mean: Process & Examples
Read this lesson to learn how you can use hypothesis testing to test for a mean. Learn what conditions need to be met before you can use hypothesis testing to find the average for the test subject.
4. Type I & Type II Errors in Hypothesis Testing: Differences & Examples
Watch this video lesson to learn about the two possible errors that you can make when performing hypothesis testing. You will see how important it is to really understand what these errors mean for your results.
5. One-Tailed Vs. Two-Tailed Tests: Differences & Examples
This lesson explores the difference between the one-tailed and two-tailed tests. We will look at what they mean in statistical testing, as well as when you should and should not use them.
6. Z Test & T Test: Similarities & Differences
A t-test is a statistical method used to see if two sets of data are significantly different. A z-test is a statistical test to help determine the probability that new data will be near the point for which a score was calculated. Learn about these two statistical calculations, their differences, and their similarities.
7. What Are t-Tests? - Assessing Statistical Differences Between Groups
This lesson explores how a researcher may use a t-Test. In addition, simple to follow instructions will demonstrate how to manually complete a t-Test.
8. Hypothesis Testing for a Proportion
Data sets can be mutually exclusive. What that means is that the population will either be or answer one thing or another. In this lesson, we'll explore how hypothesis testing is applied in that situation.
9. Hypothesis Testing for a Difference Between Two Proportions
Have you ever wondered if there's a difference between two proportions or if it just seems that way? Take for example, the proportion of college graduates who smoke vs. the proportion of college dropouts who smoke? You can test for this using hypothesis testing to discover if there's a real difference or not.
10. Hypothesis Testing Matched Pairs
Sometimes, data sets come paired. What does this actually mean, though? This lesson explains what matched pairs are and goes over an example of how hypothesis testing applies to them.
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Other chapters within the Introduction to Statistics: Help and Review course
- Overview of Statistics: Help and Review
- Summarizing Data: Help and Review
- Tables and Plots: Help and Review
- Probability: Help and Review
- Discrete Probability Distributions: Help and Review
- Continuous Probability Distributions: Help and Review
- Sampling: Help and Review
- Regression & Correlation: Help and Review