This course will provide students with an introduction to the study of modern data analysis methods in Psychology, including (but not limited to) traditional inferential statistics.

    The course roughly divided into thirds (with an exam at the end of each third). In the first third, we cover basic concepts in data analysis (including graphing, confidence limits, and the like). This section culminates in a discussion of the t-test and power (i.e. what some would call "basic hypothesis testing").
    In the second third of the course we cover linear modeling (correlation and ANOVA), those overused mainstays of data analysis in Psychology. Emphasis is placed on gaining an intuitive understading of these methods and their limitations, rather than mastering complex factorial designs or multivariate comparisons.
    In the final third of the course we do two things. First, we cover some useful data analysis techniques that fall outside the realm of traditional inferential statistics in Psychology, including bootstrap/resampling methods. Finally, we spend some time considering what role hypothesis testing should play in evaluating data.

    The textbook we use is Howell (2002; see syllabus for complete ref.), and homework problem sets are assigned weekly therefrom.

    As all sane data analysis is done by computer, the exams emphasize comprehension not computation.