CB 399: Practical Data Analysis for Experimental Biologists (or Statistics for Terrified Biologists) Summer 2013

Practical Data Analysis for Experimental Biologists (or Statistics for Terrified Biologists)
Nanocourse Director: David Van Vactor

Curriculum Fellow: Abha Ahuja, Abha_Ahuja@hms.harvard.edu

FOR THIS NANOCOURSE ONLY, AUDITORS (Post-Docs, Faculty, Staff, Students not taking the class for credit) ARE REQUIRED TO SIGN UP  AND ATTEND BOTH SESSIONS.  

Biological research is becoming increasingly quantitative. Several user-friendly statistical software packages have made it easy to apply advanced analytic methods. However some basic background is needed in order to fully harness the power of these methods. This nanocourse is designed to teach basic statistical concepts and theory in the context of real biological data and results analysis. This course will help students to:

  • Plan and design their experiments
  • Decide which statistical test to conduct
  • Interpret and understand the output from any statistical software or primary literature
  • Communicate their results accurately and effectively
  • Prepare for more advanced courses in statistics

Lectures will be interspersed with in-class exercises. The lecturer will teach each topic using real biological data. Students will explore each topic by conducting simple calculations using a calculator. At the end of each topic students will discuss a thought question or solve a problem in pairs or small groups.

Assignment:

All students are required to complete a pre- and post- course survey and attend both days. In addition, students will complete two take home assignments, one at the end of each session. They can do these assignments using a calculator, excel or their favorite statistical software.

Schedule:

First Session: Wednesday, August 21, 2013, 10 AM – 1 PM
Location: Sherman Fairchild Building, G62, Cambridge
 
Second Session: Wednesday, August 28, 2013 , 10 AM – 1 PM
Location: Sherman Fairchild Building, G62, Cambridge

Topics:

  1. Identify the question
  • Hypothesis testing framework

2. Collect relevant data

  • Experimental design

3. Describe the data

  • Central tendency and variation
  • Frequency Distributions

4. Estimating uncertainty associated with your data

  • Standard Error of a mean
  • Confidence Intervals
  • Standard Error of Difference between Means

 5. Making Decisions about your data:

  • Parametric vs. Non-parametric methods
  • The t-test
  • Wilcoxon’s Rank Sum

 

 DROP DEADLINE: Wednesday, August 14, 2013