How to design a sample to reduce bias, including response bias and wording effects.
Show practical examples of bad sample design.
Bias can skew results and lead to erroneous conclusions. By learning about the pitfalls of bias, you will improve sample design which means results from any given study will lead to more accurate results.
How do you get your daily dose of current events?
No matter the media, likely you will be getting a daily dose of statistics along with your current events. "98% of texting respondents polled agree..." or "4 out of 5 doctors surveyed said..." and to use a current example, "Beasley dealt to Wolves after LeBron picks Miami - Good for Wolves?".
Learning about unbiased sampling helps you to separate the good information from the junk most of us get on a regular basis.
For instance, take the examples I listed above:
See what I mean about knowing the details?
Source: http://www.startribune.com/?elr=KArks8c77iUec77iUiD3aPc:_Yyc:aUQ7c4E7ME5U, retrieved July 9, 2010
In statistics sampling bias is causing some members of the population to be less likely to be included than others. It results in a biased sample, a non-random sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented. If the bias makes estimation of population parameters impossible, the sample is a non-probability sample. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling.
Huh? Let me translate. Using the NBA Timberwolves example from the previous slide, sampling bias is happening big-time. Think of people who will be unlikely/unable to respond to their survey:
Or to say it another way, the only people likely to respond are those that feel very strongly about the Timberwolves and their trading activities and who have access to the internet and read the Minneapolis Star and Tribune. Kind of puts lots of these surveys
in perspective, doesn't it?
Source: http://en.wikipedia.org/wiki/Sampling_bias, retrieved July 9, 2010
Source: Bock, Velleman, DeVeaux, Stats: Modeling the World, Pearson Publishers, 2010
I found the following definitions on a blog put out by a math teacher from California. He doesn't identify himself but I would love to take a class from him. I will include the link to his blog as he has some topical (and hilarious) clips from "The Daily Show by Jon Stewart at the end of his narrative. (His reward for reading through the terms!) Be sure to read both Parts 1 & 2 as there is a second clip at the end of Part 2.
Source: http://mrho.net/blog/?p=782, retrieved July 9, 2010
A discussion of designing samples would not be complete without a section about confounding and lurking variables. Both are insidious little devils reaching out to mess with your results.
Source: http://answers.yahoo.com/question/index?qid=1006022514061, retrieved July 9, 2010