A large sample size, or N, increases the sensitivity of an experiment to detect differences between treatment groups. However, the biological entity that N refers to may not be obvious. Defining the wrong entity can inflate the sample size and increase both false-positive and false-negative results.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 1 digital issues and online access to articles
$99.00 per year
only $99.00 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Landis, S. C. et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490, 187–191 (2012).
Percie du Sert, N. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020).
Percie du Sert, N. et al. Reporting animal research: explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020).
Lazic, S. E. Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility (Cambridge University Press, 2016).
Lazic, S. E., Clarke-Williams, C. J. & Munafò, M. R. What exactly is ‘N’ in cell culture and animal experiments? PLoS Biol. 16, e2005282 (2018).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Rights and permissions
About this article
Cite this article
Lazic, S.E. Genuine replication and pseudoreplication. Nat Rev Methods Primers 2, 23 (2022). https://doi.org/10.1038/s43586-022-00114-w
Published:
DOI: https://doi.org/10.1038/s43586-022-00114-w