Explores autocorrelation, periodicity, and spurious correlations in time series data, emphasizing the importance of understanding underlying processes and cautioning against misinterpretation.
Covers basic probability theory, ANOVA, experimental design, and correlations, emphasizing the importance of planning multiple tests and power analysis.
Delves into measuring learning effects in digital education and analytics, covering research questions, variables, experimental design, and bias solutions.
Explores the design of experiments through a comic scenario and provides guidance on course materials, exercises, readings, examples, and project assignments.
