AP Statistics Ch 2.2 Sampling and Bias

census

obtaining information about the entire population

good sampling methods

everything must be chosen randomly

simple random sample (SRS)

a sample of size n is selected from the population in a way that ensures that every different possible sample of the desired size has the same chance of being selected

stratified random sample

a sample that is selected by taking a simple random sample from within each strata

strata

groups that are similar/homogenous based upon a similar characteristic of the group members. Can allow for more accurate inferences about a population than simple random sampling.

cluster sample

divide population into clusters. Randomly select an entire cluster. Then all the individuals in the clusters are included in that sample.

systematic sample

randomly select the first item in a number list. Then select every kth item. k=number of individuals in the sample frame/sample size

sampling frame

a list of all the objects or individuals in the population

numbering individuals

we number based on the number of total individuals. EX: For a random digits table, number 00-79 or 01-80.

bias

the tendency for samples to differ from the corresponding population in some systematic way

selection bias

bias created in how we selected individuals for sample. Systematically excludes a portion of the population. Portions of the population are not represented.

convenience sampling

using an easily available or convenient group to form a sample

voluntary response bias

a form of convenience sample to include only individuals who volunteer to be part of the study

measurement bias

error in the measurements collected in a study. EX: improperly calibrated scale.

response bias

error in the responses collected in a study. EX: questions worded in a way that influences response, appearance or behavior of person asking questions.

nonresponse bias

error in the way responses are collected. When responses are not obtained for the sample.