This process helps assure that the groups are similar to each other when treatment begins.Therefore, any post-study differences between groups shouldn’t be due to prior differences.
However, confounding variables have a potential upside.
They don’t sound quite so threatening when you think of them as proxy variables, which we’ll cover in my next post.
Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment.
Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment.
This highlights one of the pitfalls of ad hoc data analysis.
We’ve detailed the negative aspects of confounding variables here and in my last several posts.My past several posts have detailed confounding variables, a problem you might encounter in research or quality improvement projects.To recap, confounding variables are correlated predictors.Further, let’s assume that greater physical activity is correlated with increased bone density but we didn’t measure it. Scenario 1: We don’t use random assignment and, unbeknownst to us, the more physically active subjects end up in the treatment group.The treatment group starts out more active than the control group.In this case, we’re talking about random Random assignment might involve flipping a coin, drawing names out of a hat, or using random numbers.All subjects should have the same probability of being assigned to any group.Leaving a confounding variable out of a statistical model can make an included predictor look falsely insignificant or falsely significant.In other words, they can totally flip your statistical analysis results on its head!Random assignment helps protect you from the perils of confounding variables and competing explanations.However, you can’t always implement random assignment.