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Basics of Statistical Analysis

Diane M. Thomson1

1Joint Science Department, Claremont McKenna, Pitzer, and Scripps Colleges, Claremont, California

Publication Name: 
Current Protocols Essential Laboratory Techniques
Unit Number: 
Appendix 4B
DOI: 
10.1002/9780470089941.eta04bs03
Online Posting Date: 
June, 2010
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Abstract

The goal of this appendix is to introduce basic methods in graphing and data analysis and explore some fundamental concepts in statistical reasoning. Three examples of data-analysis problems relevant to molecular biology are used to illustrate methods covered in a first statistics course, including the two-sample t test, simple linear regression, and chi square tests for goodness of fit and contingency table hypotheses. The appendix also explores the selection and interpretation of appropriate summary graphs for these analyses, including the use of error bar plots, scatterplots, and bar charts. In addition, a number of key terms and concepts are introduced and explained in the context of the three example problems, including summary statistics, sampling variation, the standard error, null hypothesis testing, the use of test statistics, and the interpretation of p values. Curr. Protoc. Essential Lab. Tech. 3:A.4B.1-A.4B.22. © 2010 by John Wiley & Sons, Inc.

Keywords: data analysis; graphing; standard error; two-sample t test; linear regression; chi square; goodness of fit; contingency table

     
 
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Table of Contents

  • Overview and Principles
  • Where to Start: Types of Data and Analyses
  • Graphing Different Kinds of Data
  • Performing Statistical Analysis
  • Conclusion
  • Literature Cited
  • Figures
  • Tables
     
 
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Figures

  • Figure A.4B.1
    Summary graphs for the three example problems. (A) Example 1 is a comparison of continuous responses between categorical groups, best summarized with an error bar graph (mean ± one standard error). (B) Example 2 is a test of the relationship between two continuous variables, best summarized with a scatterplot. (C) Example 3 is a comparison of categorical outcomes between categorical groups, best summarized with a clustered bar graph.

    View Image
  • Figure A.4B.2
    Three hypothetical comparisons between two mutant lines, illustrated with graphs of the means ± one standard error. (A) High signal and low noise. (B) High signal and high noise. (C) Low signal and low noise.

    View Image
  • Figure A.4B.3
    Simple linear regression analysis for Example 2. (A) The best fit regression line and equation. (B) The null and alternative hypothesis, with a visual illustration of signal and noise.

    View Image

Literature Cited

Literature Cited
    Zar, J.H. 2009. Biostatistical Analysis, 5th ed. Prentice Hall, Upper Saddle River, N.J.
 Internet Resources
    http://faculty.vassar.edu/lowry/VassarStats.html

VassarStats: Website for Statistical Computation.

     
 
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