Basic Steps in Hypothesis Testing

What is the first step in hypothesis testing?

• State the null and alternative hypotheses

• Draw conclusions

• Analyze the data

• Conduct the experiment

What is the purpose of the null hypothesis?

• To serve as a default assumption for testing the validity of an alternative hypothesis.

• To confirm the alternative hypothesis

• To complicate the research process

• To provide a definitive conclusion

Define the alternative hypothesis.

• A statement that is always true.

• A statement that is accepted if the null hypothesis is accepted.

• A statement that is accepted if the null hypothesis is rejected.

• A statement that is rejected if the null hypothesis is rejected.

Explain the significance level in hypothesis testing.

• 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.

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

• The significance level is not relevant in hypothesis testing.

What is a Type I error in hypothesis testing?

• Accepting the null hypothesis when it is false

• Failing to reject the null hypothesis when it is false

• Rejecting the null hypothesis when it is true

• Rejecting the alternative hypothesis when it is true

What is a Type II error in hypothesis testing?

• A Type II error is accepting a false null hypothesis.

• A Type II error is failing to reject a false null hypothesis.

• A Type II error is rejecting a true null hypothesis.

• A Type II error is failing to reject a true null hypothesis.

Describe the process of selecting a test statistic.

• Choose the first number that comes to mind

• Identify statistical method, determine sample size and data collection method, calculate test statistic, compare to critical value or p-value.

• Ask a friend to pick a number

• Flip a coin to decide

What is the critical value in hypothesis testing?

• The critical value in hypothesis testing is the value that separates the rejection region from the non-rejection region.

• The critical value is the value that determines the p-value.

• The critical value is the value that supports the null hypothesis.

• The critical value is the value that represents the sample mean.

Explain the concept of p-value in hypothesis testing.

• The p-value is always fixed and does not change based on the data

• The p-value in hypothesis testing represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.

• The p-value is a measure of the effect size in the study

• The p-value is the probability of rejecting the null hypothesis when it is true

What is the final step in hypothesis testing?

• Review the data

• Skip to the next step

• Draw a conclusion

• Make an assumption