University:
McNeese State University
Course:
MATH 231 | Elementary Probability and Statistical Inference
Academic year:

2024
Views:

244

Pages:

1
Author:

Tori K.

Math 231 1-3 Data Collection and Experimental Design Baldwin Summary of Sampling Techniques and Potential Biases Sampling Technique Potential Bias a count or measure of an entire population Census Random Sample Simple Random Sample Stratified Sample Cluster Sample Systematic Sample Methods: asking everyone at school, US census, asking ages of residents in a nursing home, etc. every member of the population has an equal chance of being selected (entire population is sampled). Methods: randomly select members of a population takes a small, random portion of the entire data set, where each member has an equal probability of being chosen (subset if the population). Methods: lotteries or random draws members of the same population are divided into two or more subsets that share similar characteristics Methods: Socioeconomic status, age groups, gender, etc. divide the population into groups and select all the members in or more (but not all) groups Methods: School zones, parishes, wards, zip codes, etc. members of the population are ordered in some way, a starting number is randomly selected, and then sample members are selected at regular intervals from the starting number Costly and difficult to perform. Each member does not necessarily have an equal chance of being selected. Sample may not be proportional to their actual percentages of occurrence in the population. There could also be no bias. There is no bias. Samples may not have similar characteristics. Sample may have an irregular pattern occurring. Methods: Selecting every 100th person, etc. consists only of members of the population that are easy to get Convenience Sample Methods: stopping random people and asking them questions (in front of store, in classroom, etc). Sample my not be representative of the population. 7