This tool, developed by Robert Rosenthal, assists in estimating the effect size a researcher expects to observe in a study. It facilitates power analysis, allowing researchers to determine the necessary sample size to detect a statistically significant effect. For instance, if a researcher anticipates a medium effect size (e.g., Cohen’s d of 0.5), the tool can indicate the minimum number of participants needed for a desired statistical power level.
Accurate sample size estimation is critical for robust research design. Underpowered studies risk failing to detect true effects, leading to erroneous conclusions. Conversely, overpowered studies waste resources. This tool, rooted in statistical theory and practical research considerations, promotes rigorous research practices by helping researchers make informed decisions about sample size. Its use contributes to stronger evidence and more reliable scientific findings.
