Inferential Statistics: Hypothesis Testing

What is the purpose of hypothesis testing in inferential statistics?

• To determine if there is enough evidence to reject a null hypothesis in favor of an alternative hypothesis based on sample data.

• To determine if the sample data is 100% accurate

• To prove the null hypothesis is always correct

• To confuse researchers with unnecessary calculations

Explain the difference between null hypothesis and alternative hypothesis.

• The null hypothesis suggests a significant difference between variables

• The null hypothesis assumes no significant difference or relationship between variables, while the alternative hypothesis suggests there is a significant difference or relationship.

• The null hypothesis always rejects the relationship between variables

• The alternative hypothesis assumes there is no difference between variables

What is a Type I error in hypothesis testing?

• Accepting the null hypothesis when it is false

• Rejecting the alternative hypothesis when it is true

• Rejecting the null hypothesis when it is true

• Failing to reject the null hypothesis when it is false

Define p-value in the context of hypothesis testing.

• The p-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is true.

• The p-value is the probability of rejecting the null hypothesis.

• The p-value is always equal to 0.05 in hypothesis testing.

• The p-value is the same as the significance level.

What is the significance level in hypothesis testing?

• The significance level is the confidence interval in hypothesis testing.

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

• The significance level is the margin of error in hypothesis testing.

• The significance level is the p-value in hypothesis testing.

Describe the steps involved in conducting a hypothesis test.

• The steps involved in conducting a hypothesis test include stating the null and alternative hypotheses, choosing the significance level, selecting the test statistic, collecting data, calculating the test statistic, determining the p-value, making a decision based on the p-value, and drawing a conclusion.

• Choosing the null hypothesis only

• Skipping the significance level

• Drawing a conclusion before calculating the test statistic

What is a one-tailed test and when is it used in hypothesis testing?

• A one-tailed test is used in hypothesis testing when the hypothesis being tested is directional, specifying the effect's direction.

• A one-tailed test is used when the researcher wants to test multiple hypotheses

• A one-tailed test is used when the data is qualitative

• A one-tailed test is used when the sample size is small

What is a two-tailed test and when is it used in hypothesis testing?

• A two-tailed test is used when the null hypothesis states that there is a significant difference

• A two-tailed test is used when the researcher is only interested in one direction of the hypothesized value

• A two-tailed test is used in hypothesis testing when the researcher is interested in determining if there is a significant difference in both directions (greater than and less than) from the hypothesized value. It is typically used when the null hypothesis states that there is no difference or relationship.

• A two-tailed test is used when the researcher wants to test multiple hypotheses simultaneously

Explain the concept of statistical power in hypothesis testing.

• Statistical power is the likelihood of making a Type II error.

• Statistical power is the measure of sample size in hypothesis testing.

• Statistical power is the probability of accepting a true null hypothesis.

• Statistical power in hypothesis testing is the probability of correctly rejecting a false null hypothesis.

How do you interpret the results of a hypothesis test?

• Ask a Magic 8-Ball for guidance

• Compare the p-value to the significance level to determine if the null hypothesis should be rejected or not.

• Flip a coin to determine the outcome

• Count the number of participants in the study