ABSTRACT
So far, our focus in hypothesis testing and interval estimation has been on one population mean. We told the story of Dr. Sarah Bellum and Ray D. Ology, who suspected that something was wrong with a shipment of laboratory rats. They compared a sample mean for maze completion time with a known value of μ from years of research with healthy rats. In addition to computing M as the point
Introduction Pairs of Scores and the
Paired t Test Two Other Ways of Getting
Pairs of Scores Fun Fact Associated with
Paired Means Paired t Hypotheses When
Direction Is Not Predicted Paired t Hypotheses When
Direction Is Predicted Formula for the Paired t Test Confidence Interval for
the Difference in Paired Means
Comparing Means of Two Independent Groups
Independent t Hypotheses When Direction Is Not Predicted
Independent t Hypotheses When Direction Is Predicted
Formula for the IndependentSamples t Test
Assumptions Confidence Intervals for a
Difference in Independent Means
Limitations on Using the t Statistics in This Chapter
What’s Next
estimate of the population mean, the researchers computed an interval estimate of μ. The z test statistic and the confidence interval relying on a z critical value required knowledge of the numeric values of the population mean, μ, and the population standard deviation, σ. In Chapter 10, we added one twist: what if we do not know σ? The solution was to switch to the one-sample t test. Instead of using σ in the denominator, the one-sample t test uses SD, a sample estimate of σ. We used the one-sample t test to compare the mean sleep quality of medical students with the norm for healthy adults. We also found a confidence interval for the mean sleep quality, this time relying on a t critical value in the computation.
