Which strategy is commonly used to address confounding in experiments?

Which strategy is commonly used to address confounding in experiments?

• A. Random assignment
• B. Increasing sample size only
• C. Ignoring potential confounds
• D. Using a single measurement

Answer: (A)

Confounding happens when an outside variable affects both the treatment and the outcome, making it hard to tell whether observed effects come from the manipulation or from those preexisting differences. Random assignment directly combats this by making groups comparable on both measured and unmeasured factors. When participants are assigned to conditions by chance, any potential confounds are distributed roughly equally across groups, so the group differences in outcomes are more likely due to the treatment itself. This creates internal validity and supports causal interpretation. Increasing sample size helps with precision and power, but it doesn’t guarantee that groups are equivalent on confounding variables, especially in smaller studies. Ignoring potential confounds introduces bias and undermines validity. Relying on a single measurement leaves results vulnerable to error and doesn’t address systematic differences between groups. Random assignment remains the most effective strategy for balancing confounds and isolating the treatment’s effect.

Confounding happens when an outside variable affects both the treatment and the outcome, making it hard to tell whether observed effects come from the manipulation or from those preexisting differences. Random assignment directly combats this by making groups comparable on both measured and unmeasured factors. When participants are assigned to conditions by chance, any potential confounds are distributed roughly equally across groups, so the group differences in outcomes are more likely due to the treatment itself. This creates internal validity and supports causal interpretation.

Increasing sample size helps with precision and power, but it doesn’t guarantee that groups are equivalent on confounding variables, especially in smaller studies. Ignoring potential confounds introduces bias and undermines validity. Relying on a single measurement leaves results vulnerable to error and doesn’t address systematic differences between groups. Random assignment remains the most effective strategy for balancing confounds and isolating the treatment’s effect.