In (202000005), I developed skills in statistical analysis, experimental design, and scientific reporting, tailored to human data research. Below is a breakdown of the topics covered:
Orthodox Statistics: Learning descriptive measures, inferential basics (z-, t-, F-tests), ANOVA, correlation, regression, and chi-square tests using R.
Bayesian Statistics: Learned Bayes’ theorem, grid approximation, Bayes factors, and Markov Chain Monte Carlo sampling for probabilistic inference.
Error and Power Analysis: Studied Type I/II errors, power, effect sizes, and fault inflation control.
Empirical Cycle: Explored hypothesis formulation, experimentation, and theory contextualization.
Experimental Design: Covered trials, power, effect sizes, and designs (pre-, quasi-, true experiments).
Data Collection: Built and participated in an experiment on visual search asymmetries, collecting human data.
Data Processing: Analyzed experimental data in R, handling outliers and linking methods to statistics.
Visualization: Created visual plots to report results effectively.
Through this course, I gained proficiency in applying statistical and experimental techniques to human data, preparing me for AI research and my bachelor thesis.