So far in this course, we have gone through several examples illustrating the details of how significance testing works. These details included each of the following key points. Read through these key points and try tofill in the key terms that are missing. You may use some of the terms from page 345 more than once.

Use these terms:

• Type I error0Statistical power
• Type II errorPowerCentral limit theorem
• NullEffect sizeSignificance test
• Critical valueResearch

• Theallows us to predict the center, shape, and spread of the distribution of sample means (or the distribution of samplemean differences) if the null is true.
• State the theorem below.
• Thehypothesis precisely locates the center of the null distribution of sample means (or the null distribution of sample mean differences). This location is at azvalue ofor attvalue of.
• The center of thehypothesis distribution of sample means (or mean differences) is estimated from the sample mean (or sample mean difference) and cannot be known before data are collected.
• The probability of ais set by researchers when they set the value.
• By building the null and research hypothesis distributions of sample means (or sample mean differences), we can quantify the probability of failing to reject a false null (i.e.,), as well as the likelihood of rejecting a false null (i.e.,).
• Theoftorz"cuts" likely values if the null is true from unlikely values if the null is true; if a statistic (e.g.,zvalue,tvalue) is more extreme than this value, the null hypothesis is unlikely to be true.
• If the null is rejected, thehypothesis is considered likely to be true.
• After ais performed, researchers know whether or not the null is likely to be true. They do not know how effective the IV was at affecting the DV. To quantify the impact of the IV on the DV, researchers must compute a/an.