## Comparison of Means of Two Samples The t Test

The t test is used for measured variables in comparing two means. The unpaired t test compares the means of two independent samples. The paired t test compares two paired observations on the same individual or on matched individuals. In our discussion of the z test, we used as a dependent variable (DV) an intelligence quotient (IQ) test, which has a known mean and standard deviation (SD), in order to compare our sample of interest namely, readers of this magnificent opus to the general...

## Names and Numbers Types of Variables

Nominal and ordinal variables consist of counts in categories and are analyzed using nonparametric statistics. Interval and ratio variables consist of actual quantitative measurements and are analyzed using parametric statistics. Statistics provide a way of dealing with numbers. Before leaping headlong into statistical tests, it is necessary to get an idea of how these numbers come about, what they represent, and the various forms they can take. Let's begin by...

## Repeated Measures Anova

Just as we found that the paired t test, in which each subject was measured before and after some intervention, has some real advantages in statistical power (when the conditions are right), we find that there is a specific class of ANOVA designs for which repeated observations are taken on each subject. Not surprisingly, these are called repeated measures ANOVAs and are a direct extension of the paired t test. For example, suppose we repeat the ASA study only this time, each patient gets to...

## CRAP Detector XV2

A stepwise solution yielded four variables latency, total sleep time, total REM, and total time in stages 3 and 4. From those data, the researcher concluded that normal sleepers and insomniacs do not differ in the percentage of time they spend in REM and percent deep sleep. Was this correct Answer. We don't know. Since percent REM and percent deep sleep are probably correlated with the other variables, it is likely that they didn't enter into the equation because they didn't add any...

## The Kolmogorov Smirnov Test

Let's take one last run at this data set. The Kolmogorov-Smirnov test capitalizes on a cumulative frequency distribution, a term that may be unfamiliar but that can be easily understood through our example. If we were to classify the raw scores of the two samples in groups such as 5.6-6.90, 6.1-6.5, 9.6-10.0, then it is straightforward to count the number of subjects in each group who fall in each category (ie, to develop a frequency distribution). These frequencies can then be converted into a...