H1

Definition

H1 is the alternative hypothesis in a statistical hypothesis test. It represents the researcher's belief or claim about the population parameter being tested, which is the opposite of the null hypothesis (H0).

5 Must Know Facts For Your Next Test

1. The alternative hypothesis (H1) represents the researcher's claim or belief about the population parameter being tested.
2. H1 is the complement of the null hypothesis (H0), meaning that if H0 is false, then H1 must be true.
3. The alternative hypothesis can be one-sided (greater than, less than) or two-sided (not equal to) depending on the research question and the context of the study.
4. The decision to reject or fail to reject the null hypothesis is based on the strength of the statistical evidence provided by the sample data.
5. The alternative hypothesis plays a crucial role in determining the appropriate statistical test and the interpretation of the results.

Review Questions

• Explain the relationship between the null hypothesis (H0) and the alternative hypothesis (H1) in a statistical hypothesis test.
  • The null hypothesis (H0) and the alternative hypothesis (H1) are complementary statements about a population parameter. The null hypothesis represents the default or status quo position that the researcher wants to test, while the alternative hypothesis represents the researcher's claim or belief about the population parameter. If the statistical evidence is strong enough to reject the null hypothesis, then the alternative hypothesis is accepted as true. The alternative hypothesis is the opposite of the null hypothesis, meaning that if H0 is false, then H1 must be true.
• Describe the different types of alternative hypotheses and how they impact the statistical test and interpretation of results.
  • The alternative hypothesis (H1) can be one-sided or two-sided, depending on the research question and the context of the study. A one-sided alternative hypothesis specifies that the population parameter is either greater than or less than the value stated in the null hypothesis. A two-sided alternative hypothesis specifies that the population parameter is not equal to the value stated in the null hypothesis. The type of alternative hypothesis determines the appropriate statistical test to use and the interpretation of the results. For example, a one-sided alternative hypothesis may require a one-tailed test, while a two-sided alternative hypothesis requires a two-tailed test. The decision to reject or fail to reject the null hypothesis is based on the strength of the statistical evidence provided by the sample data and the chosen significance level.
• Analyze the role of the alternative hypothesis (H1) in the overall process of statistical hypothesis testing and the interpretation of the results.
  • The alternative hypothesis (H1) plays a crucial role in the statistical hypothesis testing process. It represents the researcher's claim or belief about the population parameter being tested, which is the opposite of the null hypothesis (H0). The decision to reject or fail to reject the null hypothesis is based on the strength of the statistical evidence provided by the sample data and the chosen significance level. If the evidence is strong enough to reject the null hypothesis, then the alternative hypothesis is accepted as true. The alternative hypothesis is essential for determining the appropriate statistical test, interpreting the results, and drawing conclusions about the population parameter. It also helps the researcher formulate the research question, design the study, and make inferences about the population based on the sample data.

Related terms

Null Hypothesis (H0): The null hypothesis is a statement about a population parameter that the researcher believes is true and wants to test. It is the default position that the researcher aims to disprove.

Statistical Hypothesis Test: A statistical hypothesis test is a method for making decisions about a population parameter based on sample data. It involves formulating null and alternative hypotheses and then using statistical evidence to determine whether to reject or fail to reject the null hypothesis.

Population Parameter: A population parameter is a numerical characteristic of an entire population, such as the mean, proportion, or standard deviation. Hypothesis tests are used to make inferences about population parameters based on sample data.