Adjusting for covariates is crucial for several reasons:
Reducing Bias: Properly accounting for covariates helps to minimize bias, leading to more accurate results. Clarifying Associations: Helps to isolate the effect of the primary exposure on the outcome by controlling for other influential factors. Improving Precision: Including relevant covariates can reduce the variability in the outcome, leading to more precise estimates.
