Significance Testing in Research

The video tutorial covers significance testing, focusing on tests for proportions. It explains the conditions required for these tests, such as random sampling and normality, and how to calculate test statistics and p-values. The tutorial outlines the steps in conducting a significance test: stating hypotheses, planning, executing, and concluding. It also introduces the concept of power and type 2 errors, discussing factors that affect power, like sample size and significance level.

What is the primary purpose of conducting a significance test in research?

• To test the validity of a hypothesis

• To collect data

• To create a hypothesis

• To prove a hypothesis

Which of the following is NOT a condition for conducting a significance test for proportions?

• Random sampling or assignment

• Large sample size

• Normality condition

• Independence condition

What does a test statistic measure in a significance test?

• The sample size

• The distance of the sample result from the null parameter

• The accuracy of the sample data

• The probability of the null hypothesis

Which step involves stating the null and alternative hypotheses in a significance test?

• Conclude

• Do

• Plan

• State

When is it unnecessary to run a significance test?

• When the sample size is large

• When the p-value is low

• When the null hypothesis is true

• When the sample proportion does not support the alternative hypothesis

What is a Type 2 error in hypothesis testing?

• Rejecting a true alternative hypothesis

• Rejecting a true null hypothesis

• Failing to reject a false null hypothesis

• Accepting a false alternative hypothesis

How can the power of a test be increased?

• By increasing the significance level

• By decreasing the sample size

• By lowering the p-value

• By reducing the number of variables