Error bars in experimental biology

By Geoff Cumming, Fiona Fidler, David L. Vaux. Journal of Cell Biology, 2007

968 citations recorded by the source corpus.

Error bars commonly appear in figures in publications, but experimental biologists are often unsure how they should be used and interpreted. In this article we illustrate some basic features of error bars and explain how they can help communicate data and assist correct interpretation. Error bars may show confidence intervals, standard errors, standard deviations, or other quantities. Different types of error bars give quite different information, and so figure legends must make clear what error bars represent. We suggest eight simple rules to assist with effective use and interpretation of error bars.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5ab5a6250687053b7429e4743e98fb80a4821296

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Sander Nieuwenhuis
2. Birte U. Forstmann
3. Eric-Jan Wagenmakers
4. Pierre Dragicevic
5. L. Fahrig

Citations in the corpus

• The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? (2010, background): “have also made suggestions for describing what error bars in figures should represent [5].”
• Fair Statistical Communication in HCI (2016, background): “Researchers simply need to become more consistent and get used to clearly indicating what error bars refer to (Cumming et al, 2007).”
• Erroneous analyses of interactions in neuroscience: a problem of significance (2011, methodology): “Thus, unlike Figure 1a , we can use Figure 1d to estimate the significance of the interaction by comparing the size of the gap (or in other situations the degree of overlap) between the two error bar”
• Assessing Habitat Fragmentation Effects on Primates: The Importance of Evaluating Questions at the Correct Scale (2013, background): “…the researcher to achieve narrower inferential error bars, and more precise estimates of true population values, and hence, a larger sample size leads to increased precision in estimates of population properties 2 Assessing Habitat Fragmentation Effects on Primates… 22 (Cumming et al. 2007 ).”
• If your P value looks too good to be true, it probably is: Communicating reproducibility and variability in cell biology (2019, background): “standard error of the mean or confidence intervals) (Cumming et al., 2007).”