In addition, some current software may require programming skills that are beyond the resources available to many researchers. As discussed later in the paper, oversimplified assumptions can give investigators false confidence in the chosen sample size. Some current software packages used for sample size calculations are based on oversimplified assumptions about correlation patterns. Unlike studies with independent observations, repeated measurements taken from the same participant are correlated, and the correlations must be accounted for in calculating the appropriate sample size. In spite of the advantages over cross-sectional designs, repeated measures designs complicate the crucial process of selecting a sample size. Moreover, collecting repeated measurements can simultaneously increase statistical power for detecting changes while reducing the costs of conducting a study. For instance, collecting repeated measurements of key variables can provide a more definitive evaluation of within-person change across time. Repeated measures designs are widely used because they have advantages over cross-sectional designs. Choosing the right sample size increases the chance of detecting an effect, and ensures that the study is both ethical and cost-effective. On the other hand, a study with an excessive sample size wastes resources and may unnecessarily expose study participants to potential harm. A study with an insufficient sample size may not have sufficient statistical power to detect meaningful effects and may produce unreliable answers to important research questions.
Selecting an appropriate sample size is a crucial step in designing a successful study.