In this post, we will use a practical example to explore the power of statistical control, explaining why adjusting for the right variables clarifies relationships and why adjusting for the wrong ones can introduce new, serious biases.
In this blog post, I will briefly explain why experimental designs are considered so valuable by scientists and why we often insist that randomized experiments are necessary for answering causal questions. I will use a practical example to illustrate this point. If you’re new to the concept of causal inference, it may be helpful to read my earlier blog posts on correlation vs. causation and eight basic rules for causal inference.