1. Hypothesis Testing I The One-sample Case
An Overview of Hypothesis Testing
The Five-step Model for Hypothesis Testing
One-tailed and Two-tailed Tests of Hypothesis

2. Hypothesis Testing I Selecting an Alpha Level
The Student's Distribution
Tests of Hypothesis for Single-sample Proportions

3. Five Step Model for Hypothesis Testing
Step 1. Making assumptions.
Step 2. Stating the null hypothesis.
Step 3. Selecting the sampling distribution and establishing the critical region.
Step 4. Computing the test statistic.
Step 5. Making a decision.

4. Step 1 - Making Assumptions
Assumptions must be made about the data:
Random sampling.
Level of measurement is interval-ratio.
Sampling distribution is normal.

5. Step 2 - Stating the Null Hypothesis
The entire process is aimed at rejecting or failing to reject the null hypothesis (Ho).
Research hypothesis is a statement that directly contradicts the null hypothesis.
Researcher’s goal is to gather evidence that will reject the null hypothesis.

6. Step 3 - Selecting Sampling Distribution Establishing the Critical Region
Sampling Distribution is the “yardstick” against which a particular sample outcome is measured.
Critical region consists of areas under the sampling distribution that include unlikely sample outcomes.

7. Step 4 - Computing the Test Statistic
Sample value is converted into a Z score.
Solving the equation for Z score equivalents is called computing the test statistic.
Resulting value is referred to as Z (obtained).

8. Step 5. Making a Decision
The test statistic is compared with the critical region.
If test statistic falls in the critical region - reject the null hypothesis.
If test statistic does not fall into the critical region - fail to reject the null hypothesis.

9. One-tailed Tests
Appropriate for evaluating programs that are designed to solve a problem when:
The direction of the difference can be confidently predicted.
The research is concerned only with differences in one tail of the sampling distribution.

10. Two-tailed Tests
Researcher is equally concerned about two possibilities:
The true population value is greater than the value specified in the null hypothesis.
The true population value is less than the value specified in the null hypothesis.

11. Two Errors in Hypotheses Testing
Type I, alpha - rejecting a true null.
Type II, beta - failing to reject a false null.