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Book Your Place Now IT'S FREE! TechniquesGenomics & EpigeneticsDNA / RNA Manipulation and AnalysisProtein Expression & AnalysisPCR & Real-time PCRFlow CytometryMicroscopy & ImagingCells and Model Organisms- View all of these channels -Survive & ThriveCareer Development & http://bitesizebio.com/169/error-bars-in-biology/ NetworkingDealing with Fellow ScientistsLab SafetyOrganization & ProductivityPersonal DevelopmentPhD Survival- View all of these channels -Soft Skills & ToolsBasic Lab Skills & Know-howEquipment Mastery & HacksGetting FundedLab Statistics & MathSoftware & Online ToolsTaming the LiteratureWriting, Publishing & Presenting- http://www.cyberphysics.co.uk/general_pages/errors.htm View all of these channels -Webinar Festival Get Social: Soft Skills & Tools / Lab Statistics & Math Soft Skills & Tools / Lab Statistics & MathLog InHome Error Bars in Biology By Dr Nick Oswald - error bars 9th November, 2007 ….statistics. The very word strikes fear into the heart of many a biologist (including me). In an article published earlier this year, Cumming and co-workers of La Trobe University, Melbourne gave a very useful rundown of common mistakes made when using statistical error bars in biology and suggested a number of rules that should be adhered to when presenting data in this way, especially in publications. This article provides a quick error bars on taster of their advice to try and make things seem a little less scary! Two types of error bars are commonly used in biology. Descriptive error bars used to describe a data set and inferential error bars used to determine which conclusions can be justifiably drawn from a data set. These are summarized in the table on the right, which is taken from the paper. Cumming et al suggest 8 rules that should be applied when presenting data: 1. When using error bars always describe what type they are in the figure legend 2. The value of n (the sample size) should always be stated in the figure legend 3. Error bars and statistics should only be shown for independently repeated experiments and never for replicates. If a "representative" experiment shown, it should not have error bars or P values because in such an experiment n=1 The reason for this rule is summed up quite well in the paper: "Consider trying to determine whether deletion of a gene in mice affects tail length. We could choose one mutant mouse and one wild-type mouse, and perform 20 replicate measurements of each of their tails. We could calculate [the mean and error bars], but these would not permit us to answer the central question… because n would equal 1 for each genotype
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