hypotheses, experiments, and disproof
Collecting and displaying data
Introductory concepts of experimental design
Probability helps you make a decision about your results
data, populations, and statistics
tests for comparing the means of one and two samples
Type 1 and type 2 errors, power, and sample size
Single factor analysis of variance
Multiple comparisons after ANOVA
Two factor analysis of variance
Important assumptions of analysis of variance: transformations and a test for equality of variances
Two factor analysis of variance without replication, and nested analysis of variance
Relationships between variables: linear correlation and linear regression
Non-parametric statistics
Non-parametric tests for nominal scale data
Non-parametric tests for ratio, interval, or ordinal scale data
Doing science responsibly and ethically.
