t-tests
The lessons offered in the t-tests section introduce inferential statistics. In the prior chapters, our use of measures of central tendency (i.e., mean, median, mode) and variance (i.e., range, variance, standard deviation) serve to describe a sample.
As we move into inferential statistics we evaluate data from a sample and try to determine whether or not we can use it to draw conclusions (i.e, predict or make inferences) about a larger, defined, population.
The t-test lessons begin with an explanation of the z-score and progress through one sample, independent samples, and paired samples t-tests. Each lesson is centered around a research vignette that was focused on physicians’ communication with patients who were critically and terminally ill and in the intensive care unit at a hospial (Elliott et al., 2016).
In addition to a conceptual presentation of of each statistic, each lesson includes:
- a workflow that guides researchers through decision-points in each statistic,
- the presentation of formulas and R code for “hand-calculating” each component of the formula,
- script for efficiently computing the statistic with R packages,
- an “recipe” for an APA style presentation of the results,
- a discussion of power in that particular statistic with R script for calculating sample sizes sufficient to reject the null hypothesis, if in fact, it is appropriate to do so, and
- suggestions for practice that vary in degree of challenge.