Understanding Hypothesis

What is the purpose of a null hypothesis?

• To establish causation between variables

• To determine the effect size of a relationship

• To confirm the research hypothesis

• To test the validity of an assumption by assuming there is no significant difference or relationship between variables.

Explain the concept of an alternative hypothesis.

• An alternative hypothesis is always proven to be true in a research study.

• An alternative hypothesis is a statement that suggests there is a statistically significant relationship between two variables in a study, indicating that the null hypothesis is not true.

• An alternative hypothesis is a statement that suggests there is no relationship between two variables in a study.

• An alternative hypothesis is the same as the null hypothesis.

What is a Type I error and how does it relate to hypothesis testing?

• A Type I error is rejecting a true null hypothesis, and it relates to hypothesis testing through the significance level (alpha).

• A Type I error is accepting a false null hypothesis, and it relates to hypothesis testing through the power of the test.

• A Type I error is failing to reject a false null hypothesis, and it relates to hypothesis testing through the p-value.

• A Type I error is rejecting a false null hypothesis, and it relates to hypothesis testing through the confidence interval.

Describe a Type II error and its implications in hypothesis testing.

• A Type II error occurs when the null hypothesis is rejected when it is actually true.

• A Type II error leads to the conclusion that there is a significant effect when there is none.

• A Type II error in hypothesis testing occurs when the null hypothesis is not rejected when it is actually false.

• A Type II error indicates that the sample size was too small to detect a true effect.

How do Type I and Type II errors impact the conclusions drawn from a hypothesis test?

• Type I error leads to false negatives

• Type II error leads to false positives

• Type I error leads to false positives, while Type II error leads to false negatives, impacting the accuracy of the conclusions drawn from a hypothesis test.

• Type I error has no impact on conclusions