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LISA Statistics Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size
(Academic)
LISA SHORT COURSES IN STATISTICS
LISA (Virginia Tech's Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data. See www.lisa.stat.vt.edu/?q=short_courses for instructions on how to REGISTER and to learn more.
Spring 2016 Schedule:
Tuesday, March 15, 4:00-6:00 pm: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size;
Tuesday, March 22, 4:00-6:00 pm: Data Analytics Classification;
Tuesday, March 29, 4:00-6:00 pm: Basics of R;
Tuesday, April 5, 4:00-6:00 pm: Statistical Analysis Using R;
Tuesday, April 12, 4:00-6:00 pm: Better Data Visualization in R Using the ggplot2 Package;
Tuesday, April 19, 4:00-6:00 pm: Introduction to Web Scraping in R;
Tuesday, April 26, 4:00-6:00 pm: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP;
Tuesday, March 15, 4:00-6:00 pm;
Location: 1100 Torgersen Hall;
Instructor: Ting Guan;
Title: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size;
Motivation: Frequently, comparing samples from two populations is of interest. For example, is the average height in the US different from that in China? If they are different, how different?
Goal: In this short course, we will compare averages and other measures of location using significance tests and effect size estimates.
In classical hypothesis testing, significance tests assume the null hypothesis is true. Then the probability that data as extreme as or more extreme than those observed is calculated. If this probability is very low, then we reject the null hypothesis. For the two sample case, we will focus on the following methods and questions:
Parametric method: two-sample t-test
- What are the null hypothesis and alternative hypothesis of a two-sample t-test?
- What are the assumptions of two-sample t-test?
- How do we check if these assumptions hold?
- Should we use pooled or unpooled estimates for standard deviation?
- How to run two-sample t-test in R?
- What effect size estimate is appropriate in this scenario?
Nonparametric method: Mann-Whitney U test
- What are the null hypothesis and alternative hypothesis of a Mann-Whitney U test?
- What are the assumptions of Mann-Whitney U test?
- How to check if these assumptions hold or not?
- How to run Mann-Whitney U test in R?
- What effect size estimate is appropriate in this scenario?
We will use the following datasets:
- Data from a lung cancer study at VA (http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv).
- Data from a soil respiration study (http://www.stat.purdue.edu/~tqin/system101/method/data/soil.csv).
- Data from a study on high school students (http://www.ats.ucla.edu/stat/r/modules/hsb2.csv).
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540-231-8354