What is a key strength of independent measures design?

A key strength of independent measures design is that it minimizes bias from previous conditions. In this research design, different participants are assigned to each condition of the experiment. This means that each participant experiences only one condition and does not carry over any effects from one condition to another. As a result, the participant's performance is not influenced by previous experiences or manipulations within the study, reducing the risk of confounding variables affecting the results.

This is particularly important because it allows for a clearer interpretation of the data, as any differences in outcomes can more confidently be attributed to the specific experimental manipulation being studied rather than other extraneous factors related to individual differences or prior exposure to the experimental conditions.

In contrast, the other options focus on aspects that do not fundamentally capture this crucial strength of the independent measures design. For instance, while simplification of data analysis may arise from a clear separation of conditions, it isn't a primary strength unique to this design type. Additionally, independent measures typically require more participants overall, as each participant's data is generated from only one of the conditions, which does not align with the idea of fewer participants. Finally, independent measures still often utilize control groups to compare against experimental conditions, so the elimination of the need for a control group