The effect of trial size on statistical power

Area of Science:

• * Research methodology and statistical analysis.
• * Experimental design and data interpretation.

Background:

• * Many studies lack the statistical power to detect true effects, leading to erroneous support for the null hypothesis.
• * Understanding the interplay between sample size, data reliability, and analytical approach is crucial for accurate research findings.

Purpose of the Study:

• * To demonstrate the relationship between single-subject (SS) and group analyses.
• * To emphasize the critical role of data reliability (trial size) in achieving statistical power.
• * To investigate the influence of sample size, variability, and performance strategies on statistical outcomes.

Main Methods:

• * Development of a computer model utilizing Monte Carlo procedures.
• * Simulation of various factors including sample size (subjects and trials), within- and between-subject variability, and performance strategies.
• * Evaluation of selected statistical procedures under simulated conditions.

Main Results:

• * Group analyses with similar performance strategies require 10, 5, and 3 trials for sample sizes of 5, 10, and 20 subjects, respectively, to achieve >90% power for effect sizes of one standard deviation.
• * Single-subject (SS) analyses demonstrated significantly lower statistical power compared to group analyses for equivalent trial sizes.
• * Detecting significant differences is considerably more challenging with SS designs.

Conclusions:

• * Adequate trial size is essential for achieving sufficient statistical power in research, particularly in group analyses.
• * Single-subject designs necessitate larger sample sizes or more trials to reach the statistical power of group designs.
• * Researchers must carefully consider statistical power when interpreting nonsignificant findings, especially in SS studies.