Confounding is a third variable affecting the relationship between predictor and outcome variables
Randomization of research participants is the best way to deter confounding effects in applied research and statistics. When random selection and random assignment of study participants are employed, any differences between the groups is thought to occur purely by chance. Therefore, any known or unknown confounding effects will be similar across the randomized participants and groups.
In observational or non-randomized designs, multivariate analysis is the best method to account for confounding. And confounding is bound to exist in these types of designs because of selection and observation biases associated with their methods.
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