
Statistical Power in Research: Insights from the Lund Biomedicine ReproducibiliTea Journal Club
jan 24
2 min läsning
4
22
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In the latest gathering of the Lund Biomedicine ReproducibiliTea Journal Club, researchers delved into a critical aspect of scientific methodology: statistical power. Inspired by Button et al.'s paper "Power Failure: Why Small Sample Size Undermines the Reliability of Neuroscience," (1) the discussion illuminated the nuanced challenges of research design and statistical analysis.
Key Takeaways on Statistical Power
Low statistical power reduces both the likelihood of detecting true effects and the credibility of statistically significant findings. The session, led by Daniela Grassi, unpacked several crucial considerations for researchers:
1. Optimal Study Power: A Nuanced Perspective
Statistical power isn't a one-size-fits-all concept. Its optimal level varies depending on the research field and study stage. While 80% power is generally considered acceptable, the reality is far more complex. There are situations when lower power may be unavoidable due to study limitations. In some cases, it might even be better to perform multiple studies at low power than to try and increase the power of a single study.
2. Preclinical Research: A Power Calculation Blind Spot
Anecdotal evidence suggests that preclinical and exploratory studies frequently overlook power calculations. This practice, while common, raises important questions about research reliability and reproducibility.
3. Transparency in Sample Size Determination
Researchers must be transparent about their sample size selection. Although an a priori power calculation represents the gold standard, other acceptable methods exist. The key is providing a clear, justifiable rationale for the chosen sample size.
4. Statistical Test Selection: Planning is Paramount
Statistical tests should be predetermined, ideally specified in the study protocol before data collection begins. This approach ensures methodological rigor and prevents post hoc statistical manipulation.
Join the Conversation
Curious about diving deeper into proper methodological concepts? Our upcoming journal club meetings promise to continue exploring these critical research questions. .
We invite researchers, students, and science enthusiasts to join our discussions on improving scientific methods, increasing reproducibility, and advancing the Open Science movement. Register your interest to receive updates about upcoming ReproducibiliTea Journal Club meetings, including our upcoming session on systematic reviews (February 26th).
References
Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013 May;14(5):365-76. Epub 2013 Apr 10. doi: 10.1038/nrn3475.
Speaker

Daniela Grassi, PhD, has a background in neuroscience and a long-standing passion for metascience and improving research practices.
Authors

Rebeca Cardoso, PhD, is a Research Consultant at AdvanSci Research Solutions. Committed to making science accessible, Rebeca strives to bring cutting-edge research to a broader audience through her writing and outreach initiatives.

Sean Kim, PhD, is a Senior Medical Writer at AdvanSci Research Solutions. He has over 17 years of expertise in crafting professional scientific communications across a wide range of medical topics.