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. 2022 Oct;31(10):2917-2929.
doi: 10.1007/s11136-021-03020-y. Epub 2021 Oct 29.

Power(ful) myths: misconceptions regarding sample size in quality of life research

Samantha F Anderson  1
Affiliations

Power(ful) myths: misconceptions regarding sample size in quality of life research

Samantha F Anderson. Qual Life Res. 2022 Oct.

Abstract

Purpose: Carefully selecting the sample size for a research study is one of the most fundamental ways to utilize resources in an ethical manner, maximize impact and replicability, and minimize research waste when investigating questions relevant to health-related quality of life (HRQOL). Despite an increased focus on sample size in the methodological literature, the topic has received limited attention in the HRQOL field, and there are still misconceptions that can weaken even well-intentioned sample size planning. This article aims to highlight common misconceptions, provide accessible and non-technical corrections to these misconceptions, and show how HRQOL researchers can benefit from a more nuanced understanding of sample size planning.

Method: Misconceptions were identified broadly through examples within the health, psychology, and HRQOL literatures. In examining these misconceptions, study-level (e.g., missing data, multilevel designs, multiple reported outcomes) and field-level (e.g., publication bias, replicability) issues relevant to HRQOL research were considered.

Results: Misconceptions include: (a) researchers should use rules of thumb or the largest sample size possible, (b) sample size planning should always focus on power, (c) planned power = actual power, (d) there is only one level of power per study, and (e) power is only relevant for the individual researcher. Throughout the article, major themes linked to these misconceptions are mapped onto recent HRQOL studies to make the connections more tangible.

Conclusion: By clarifying several challenges and misconceptions regarding sample size planning and statistical power, HRQOL researchers will have the tools needed to augment the research literature in effective and meaningful ways.

Keywords: Accuracy; Effect size; Research waste; Sample size; Statistical power.

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