Experimental Design in Python

About This Course

This course offers an in-depth understanding of experimental design and statistical analysis, equipping you to draw accurate conclusions from your data. You will investigate various experimental design frameworks, including randomized block designs and factorial designs, which are tailored for evaluating treatment effects. The curriculum encompasses statistical analyses of experimental data, focusing on the selection of appropriate statistical tests, conducting post-hoc analyses, and calculating effect sizes while determining the minimum sample sizes through Cohen's d and power analysis. Moreover, you will learn how to derive valuable insights from complex experimental data, effectively communicate your findings to stakeholders, and address challenges such as interactions, heteroscedasticity, and confounding variables. You will also apply nonparametric methods in situations where the assumptions of parametric tests do not hold.