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Laboratory Studies 
Recordkeeping,
Writing, & Data Analysis 
Laboratory Methods 

Overview Microscope studies Flagella experiment Laboratory math Blood fractionation Gel electrophoresis Protein gel analysis Mitochondria Concepts/ theory 
Overview Keeping a lab notebook Writing research papers Dimensions & units Using figures (graphs) Examples of graphs Experimental error Representing error Applying statistics 
Overview Principles of microscopy Solutions & dilutions Protein assays Spectrophotometry Fractionation & centrifugation Radioisotopes and detection 

Statistical tests
Tables

Selected Statistical Methods for the BiosciencesErrors using inadequate data are much less than
those using no data at all.  Charles Babbage (17921871)
Statistics are designed to draw conclusions from data that are subject to experimental error. When replicate sampling is necessary, a carefully chosen statistical analysis can save the investigator the trouble of performing unnecessary experiments, or the embarrassment of drawing a premature or inappropriate conclusion. Statistics generally yield a probability value for a particular outcome. As a rule, scientists accept a probability of 0.05 or less as convincing evidence that a particular outcome is unlikely. Information on selected types of statistical analysis is presented on these pages. Do not expect a rigorous explanation of the theory behind each type of analysis. The articles will focus on an understanding of the principles behind the analyses, and on their proper use. 

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and Intended Use Visitors: to ensure that your message is not mistaken for SPAM, please include the acronym "Bios211" in the subject line of email communications Created by David R. Caprette (caprette@rice.edu), Rice University Dates 