Interpreting Standard Deviation Error Bars
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Overlapping Error Bars
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How To Calculate Error Bars
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Error Bars Standard Deviation Or Standard Error
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How To Draw Error Bars
7–11. doi: 10.1083/jcb.200611141PMCID: PMC2064100FeaturesError bars in experimental biologyGeoff Cumming,1 Fiona Fidler,1 and David L. Vaux21School of what do small error bars mean Psychological Science and 2Department of Biochemistry, La Trobe University, Melbourne, Victoria, Australia 3086Correspondence may also be addressed to Geoff Cumming (ua.ude.ebortal@gnimmuc.g) or Fiona Fidler (ua.ude.ebortal@reldif.f).Author information ► Copyright https://www.youtube.com/watch?v=u1jrrr-Fgb8 and License information ►Copyright © 2007, The Rockefeller University PressThis article has been cited by other articles in PMC.AbstractError 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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2064100/ 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.What are error bars for?Journals that publish science—knowledge gained through repeated observation or experiment—don't just present new conclusions, they also present evidence so readers can verify that the authors' reasoning is correct. Figures with error bars can, if used properly (1–6), give information describing the data (descriptive statistics), or information about what conclusions, or inferences, are justified (inferential statistics). These two basic categories of error bars are depicted in exactly the same way, but are actually fundamentally different. Our aim is to illustrate basic properties of figures with any of the common error bars, as summarized in Table I, and to explain how they should be used.Table I.Common er
> 2.1: Molecules to Metabolism 2.2: Water 2.3: Carbohydrates and Lipids 2.4: Proteins 2.5: Enzymes 2.6: DNA http://www.biologyforlife.com/interpreting-error-bars.html and RNA 2.7: DNA Replication, Transcription and Translation 2.8: Cell Respiration 2.9: http://www.graphpad.com/support/faqid/1362/ Photosynthesis 3: Genetics > 3.1: Genes 3.2: Chromosomes 3.3: Meiosis 3.4: Inheritance 3.5: Genetic Modification and Biotechnology 4: Ecology > 4.1: Species, Communities and Ecosystems 4.2: Energy Flow 4.3: Carbon Cycling 4.4: Climate Change 5: Evolution and Biodiversity > 5.1: Evidence for Evolution 5.2: Natural Selection error bars 5.3: Classification and Biodiversity 5.4: Cladistics 6: Human Physiology > 6.1: Digestion and Absorption 6.2: The Blood System 6.3: Defense Against Infectious Disease 6.4: Gas Exchange 6.5: Neurons and Synapses 6.6: Hormones, Homeostasis and Reproduction Higher Level > 7: Nucleic Acids > 7.1: DNA Structure and Replication 7.2: Transcription and Gene Expression 7.3: Translation 8: Metabolism, Cell interpreting standard deviation Respiration & Photosynthesis > 8.1: Metabolism 8.2: Cell Respiration 8.3: Photosynthesis 9: Plant Biology > 9.1: Transport in the Xylem of Plants 9.2: Transport in the Phloem of Plants 9.3: Growth in Plants 9.4: Reproduction in Plants 10: Genetics and Evolution > 10.1: Meiosis 10.2: Inheritance 10.3: Gene Pools and Speciation 11: Animal Physiology > 11.1: Antibody Production and Vaccination 11.2: Movement 11.3: Kidney and Osmoregulation 11.4: Sexual Reproduction Options > D: Human Physiology > D.1: Human Nutrition D.2: Digestion D.3: Functions of the Liver D.4: The Heart D.5: Hormones and Metabolism D.6: Transport of Respiratory Gases IB Requirements Learner Profile Group 4 Project Extended Essay External Exam Internal Assessment > Personal Engagement Exploration Analysis Evaluation Communication Investigation Skills Lab Safety Microscopy Lab Drawings Data Tables Measurement Statistics > Mean Standard Deviation T-Test ANOVA Correlation X2 Goodness of Fit X2 Test for Independence Graphing > Graphing with Excel Interpreting Error Bars Error Analysis Course Info Above & Beyond > Biology Club Pumpkin Carving Scavenger Hunt Science News IB Bio Dance Wo
Graphpad.com FAQs Find ANY word Find ALL words Find EXACT phrase What you can conclude when two error bars overlap (or don't)? FAQ# 1362 Last Modified 22-April-2010 It is tempting to look at whether two error bars overlap or not, and try to reach a conclusion about whether the difference between means is statistically significant. Resist that temptation (Lanzante, 2005)! SD error bars SD error bars quantify the scatter among the values. Looking at whether the error bars overlap lets you compare the difference between the mean with the amount of scatter within the groups. But the t test also takes into account sample size. If the samples were larger with the same means and same standard deviations, the P value would be much smaller. If the samples were smaller with the same means and same standard deviations, the P value would be larger. When the difference between two means is statistically significant (P < 0.05), the two SD error bars may or may not overlap. Likewise, when the difference between two means is not statistically significant (P > 0.05), the two SD error bars may or may not overlap. Knowing whether SD error bars overlap or not does not let you conclude whether difference between the means is statistically significant or not. SEM error bars SEM error bars quantify how precisely you know the mean, taking into account both the SD and sample size. Looking at whether the error bars overlap, therefore, lets you compare the difference between the mean with the precision of those means. This sounds promising. But in fact, you don’t learn much by looking at whether SEM error bars overlap. By taking into account sample size and considering how far apart two error bars are, Cumming (2007) came up with some rules for deciding when a difference is significant or not. But these rules are hard to remember and apply. Here is a simpler rule: If two SEM error bars do overlap, and the sample sizes are equal or nearly equal, then you know that the P value is (much) greater than 0.05, so the difference is not statistically significant. The opposite rule does not apply. If two SEM error bars do not overlap, the P value could be less than 0.05, or it could be greater than 0.05. If the sample sizes are very different, this rule of thumb does not always work.