Hypothesis Testing Concepts

Define hypothesis testing.

• Hypothesis testing is a statistical method used to make inferences about a population parameter based on sample data.

• Hypothesis testing is a method used to prove a theory with absolute certainty

• Hypothesis testing involves guessing the outcome of an experiment

• Hypothesis testing is only applicable in social sciences

What is the null hypothesis?

• The null hypothesis is the alternative hypothesis

• The null hypothesis is always proven correct

• The null hypothesis is only used in qualitative research

• The null hypothesis states that there is no effect or no difference.

Explain the concept of alternative hypothesis.

• The alternative hypothesis is a statement that is only used in observational studies.

• The alternative hypothesis is a statement that is accepted when the null hypothesis is rejected.

• The alternative hypothesis is a statement that is never tested.

• The alternative hypothesis is a statement that is always true.

Differentiate between one-tailed test and two-tailed test.

• One-tailed test focuses on both sides of the distribution, while two-tailed test focuses on one side.

• One-tailed test is more accurate than two-tailed test.

• One-tailed test focuses on one specific side of the distribution, while two-tailed test considers both sides.

• One-tailed test is used for continuous data, while two-tailed test is used for categorical data.

What is a type I error in hypothesis testing?

• Rejecting a true null hypothesis

• Rejecting a false null hypothesis

• Accepting a false null hypothesis

• Accepting a true null hypothesis

Define type II error and provide an example.

• Type II error is when the null hypothesis is incorrectly accepted when it is actually false.

• Type II error is when the p-value is greater than the significance level.

• Type II error is when the null hypothesis is correctly accepted when it is actually true.

• Type II error is when the alternative hypothesis is incorrectly accepted when it is actually false.

Explain the significance level in hypothesis testing.

• The significance level is not relevant in hypothesis testing.

• The significance level is the probability of accepting the null hypothesis when it is actually false.

• The significance level is always set at 0.5 in hypothesis testing.

• The significance level in hypothesis testing is the probability of rejecting the null hypothesis when it is actually true.

What is a p-value and how is it used in hypothesis testing?

• A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is false. It is used in hypothesis testing to determine the validity of the data.

• A p-value is the probability of obtaining results as extreme as the observed results, assuming the alternative hypothesis is true. It is used in hypothesis testing to determine the significance of the results.

• A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true. It is used in hypothesis testing to determine the significance of the results.

• A p-value is a fixed value used to determine the sample size needed for a hypothesis test. It is used in hypothesis testing to calculate the margin of error.

Define critical region in hypothesis testing.

• The critical region is the range of values where the null hypothesis is accepted.

• The critical region is the set of all possible values of the test statistic that would lead to rejecting the null hypothesis.

• Critical region is the area where the p-value is calculated.

• Critical region is the region where the alternative hypothesis is rejected.

How is a confidence interval related to hypothesis testing?

• A confidence interval is only applicable when the sample size is small in hypothesis testing

• A confidence interval provides a range of values within which the true population parameter is likely to fall, while hypothesis testing is used to make inferences about a population parameter based on sample data.

• A confidence interval is used to determine the significance level of a hypothesis test

• A confidence interval always leads to the rejection of the null hypothesis in hypothesis testing