Statistics

Statistics

Chapter 8: Hypothesis Testing

In order to make a decision or interpretation about the data, you have to test a hypothesis. Hypothesis testing allows the data to be interpreted. The result of an experiment is considered statistically significant if it is not likely to have occurred by chance alone. That means the result was influenced by the experimental factor or dependent variable. We will focus on specific terms associated with hypothesis testing and how to define and interpret a hypothesis. You may want to refer back to Chapters 1 through 7 as we proceed.

Learning Objectives

After reading Chapter 8 and completing the workbook, you should be able to:

1. Explain the fundamentals of hypothesis testing. 2. Explain the difference between the null and alternative hypothesis. 3. Define p-value.

4. Define basic hypothesis terms. 5. Define basic hypothesis test. 6. Apply hypothesis testing.

Study Clues

A clear understanding of the basic statistical terms and hypothesis test presented in Chapter 8 is important to the overall understanding of the statistics presented in this course. As you study, you should pay particular attention to the definitions and application of the tests. Refer back to the previous chapters for a review of terms and concepts.

8.1 Hypothesis

A hypothesis test includes all possible outcomes. There are two types of hypotheses:

Null hypothesis

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Alternative hypothesis

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8.2 P-value

The p-value is the same as the probability value. We accept or reject the hypothesis based on the outcome of the p-value. If the p-value is less than the required significance level, usually the significance level is 0.05, the null hypothesis is rejected and the alternative hypothesis is accepted. However, if the p-value is not less than the significance level, typically anything above 0.05, then the null hypothesis is not rejected.

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