Which method for controlling extraneous variables allows researchers to control a multitude of variables simultaneously while not requiring specific attention to each extraneous variable?

The method that allows researchers to control a multitude of extraneous variables simultaneously without requiring specific attention to each one is randomization. Randomization involves assigning participants to different groups in a way that each participant has an equal chance of being placed in any group. This process helps distribute the effects of extraneous variables evenly across all groups, which minimizes their potential biases on the results of the study.

By randomly assigning participants, researchers can effectively reduce the influence of variables that could confound the results, such as individual differences in age, gender, or health status. This creates a scenario where the only systematic difference between groups is the treatment or intervention being studied, enhancing the internal validity of the experiment.

While other methods such as holding constant, matching, and statistical adjustment also address extraneous variables, they typically require more direct intervention or consideration of specific variables. For example, holding constant means controlling certain variables at a specific level, which can be limiting and does not account for the variability these factors can introduce. Matching involves pairing participants based on particular characteristics, which can be cumbersome and might not always be feasible in practice. Statistical adjustment makes use of statistical techniques after data collection to account for extraneous variables, but this approach can only adjust for measured variables and relies on the