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<p>Bootstrapping offers a flexible approach to estimating statistical power, when planning a new study based on a previous one. In this article, we explore both parametric and non-parametric bootstrap power calculations and demonstrate how to incorporate uncertainty about effect size in bootstrap power analysis. We use real examples to illustrate bootstrap power calculations in independent <i>t</i>-test and ANCOVA. Finally, we discuss the broader implications of bootstrap power calculation for a variety of research designs.</p>

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Bootstrap power calculation: a flexible alternative to conventional power analysis for prospective and replication studies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Xiaofeng Steven Liu

摘要

Bootstrapping offers a flexible approach to estimating statistical power, when planning a new study based on a previous one. In this article, we explore both parametric and non-parametric bootstrap power calculations and demonstrate how to incorporate uncertainty about effect size in bootstrap power analysis. We use real examples to illustrate bootstrap power calculations in independent t-test and ANCOVA. Finally, we discuss the broader implications of bootstrap power calculation for a variety of research designs.