When planning an experiment, you always face a balancing act between statistical power, cost, and complexity. Statistical power is the probability that your experiment will detect a true effect if one exists. Higher power reduces the risk of missing important findings, but achieving it usually means increasing your sample size or adding more measurement points. This, in turn, raises the cost — not just in money, but also in time and resources. Complexity refers to how many factors, levels, or interactions you include in your design. More complex designs can answer more nuanced questions, but they demand more data, introduce more opportunities for mistakes, and often require greater expertise to analyze and interpret.

As you add more factors or levels to your experiment, the number of experimental conditions grows rapidly. For example, testing three factors at two levels each results in eight conditions, but adding just one more factor doubles that to sixteen. This exponential growth impacts both the resources needed and the challenge of keeping your data clean and interpretable. You must weigh whether the extra insights gained from a more complex design are worth the added cost and risk of error.

To help clarify these trade-offs, review the following table. It compares several experimental designs with respect to power, cost, complexity, and typical use cases.

This table shows that as you move toward higher power and increased complexity, costs also rise. Simpler designs are less expensive and easier to execute, but may not capture all important effects. More complex designs can reveal deeper insights but require more resources and careful planning.