Recordkeeping, Writing,
& Data Analysis


Microscope studies

Flagella experiment
Laboratory math
Blood fractionation
Gel electrophoresis
Protein gel analysis
Concepts/ theory
Keeping a lab notebook
Writing research papers
Dimensions & units
Using figures (graphs)
Examples of graphs
Experimental error
Representing error
Applying statistics
Principles of microscopy

Solutions & dilutions
Protein assays
Fractionation & centrifugation
Radioisotopes and detection

Statistical tests




Selected Statistical Methods for the Biosciences

Errors using inadequate data are much less than those using no data at all. --- Charles Babbage (1792-1871)

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.

Copyright and Intended Use
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Created by David R. Caprette (caprette@rice.edu), Rice University Dates