Georgetown Pharmacology
Program Overview
Faculty and Research
Graduate Studies
General Information
Admissions Information
Course Information
Student Handbook (PDF)
Students of IPN
News and Information


PHAR-529           2 CREDITS     Fall 2009

 

Applied Statistical Principles in Pharmacology
 

SEE DANIEL PAK (C405; 7-8750; dtp6@georgetown.edu)
Fridays 1 pm to 3 pm       NE 401 Med/Dent

 

I. METHODS 

1. September 4 - WOLFE                                                                            

Molecular biology

         DNA, RNA, cloning; northern blotting; PCR, probes.   

        

2. September 18 - XU                                                                                  

Mouse genetics

            Generation and testing of mouse models.

 

3. September 25 - WOLFE                                                                          

Proteins and antibodies

            Making antibodies, western blotting, immunoprecipitation,
             immunostaining, protein-protein interactions.

 

II. EXPERIMENTAL DESIGN 

1. October 2, 9 - PAK

Experimental Design Concepts                                         

Planning experiments: Validity, threats and controls; Statistical vs experimental errors;  n numbers and p values; t-tests, paired and unpaired..

 

2. October 16, 23 - WOLFE

How to choose statistical tests, analyze and troubleshoot your data

Systematic error, data scatter, medians vs. means; data transformations: log, normalization; alternate hypotheses.

 

3. October 30, November 13 - MALKOVA

ANOVAs and multiple group comparisons

One-way ANOVA single and repeated measures; Two-way ANOVA; Post hoc tests: Tukey, Bonferroni, Student-Newman Keuls, Dunnett.

 

4. November 20, December 4 Note 2:00 start on 12/4 -  DRETCHEN

Non-parametric statistics

Compare and contrast the appropriate use of parametric and non-parametric statistics (underlying assumptions, power and criteria for use); Integral, nominal and ordinal scales; Sign Test, Wilcoxon Matched Pair Sign Rank Test and the Mann-Whitney U Test as alternatives to the parametric t test; Chi Square Test for multiple comparisons.