Improving your statistical inferences

This course offers comprehensive training on statistical inference, covering various topics such as interpreting p values, effect sizes, Bayes Factors, and confidence intervals. It also delves into the use of likelihood ratios to answer different questions and the design of experiments to minimize false positive rates. Additionally, the course teaches you how to determine sample size for high statistical power and how to conduct p curve analyses to interpret scientific literature affected by publication bias.

Other important topics covered include theory construction, cumulative science, and philosophy. You will learn about replication studies, pre-registering experiments, and sharing results using Open Science principles. The course also includes practical exercises on simulating t tests and calculating likelihood rates as well as discussing the positive predictive value for research findings. Sequential analyses are taught to avoid problems with optional stopping.

Furthermore, the course emphasizes the importance of a-priori power assessments and provides guidance on calculating effect sizes, confidence intervals, and other details through simulations. Bayesian statistics and equivalence testing are covered to verify the null hypothesis.

With over 30,000 students already enrolled, this course is suitable for anyone interested in improving their statistical inference skills. The instructor also offers another course titled "Improving your Statistical Questions." Chinese subtitles are available for all videos.