Understanding Statistics & Experimental Design

This lecture covers the impact of unequal variances on Type I error rates in hypothesis testing, the importance of replication in scientific studies, the concept of power in hypothesis testing, and the effects of effect size on statistical power. It also discusses biases such as optional stopping, publication bias, and data peeking, and their implications on research outcomes. The instructor presents simulated experiments to demonstrate these concepts and highlights the challenges of interpreting statistical results in the presence of biases.