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Statistical Power in Research: Insights from the Lund Biomedicine ReproducibiliTea Journal Club

jan 24

2 min läsning

4

22

0


Small sample sizes can have serious consequences on the validity of medical research conclusions.
Small sample sizes can have serious consequences on the validity of medical research conclusions.

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

  1. 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

A research consultant and science writer standing in front of ivy.

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






Authors

A research consultant and science writer standing in front of ivy.

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.


A research consultant and science writer standing in front of ivy.

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.

jan 24

2 min läsning

4

22

0

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