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AP Stats chapter 12- sample surveys
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Terms in this set (22)
statistical inference
provides ways to provide "reasonable" responses to specific question by examining data
population
a group from which info is desired
census
a sample that consists of the entire population. can use when population is small.
parameter
a number used in a model for a population
sample
part of a population that examined in an attempt to obtain info abt the population
sample survey
a study that asks questions of a sample of the population
ex. Poll taken to assess voter preferences
statistic
a summary computed from the data
sampling frame
individuals from whom the sample is drawn; they should be in the population of interest
sampling variability
we will not get the same each time? sometimes called sampling errow-it really isnt error- its the natural tendency that randomly drawn samples differ (vary) from one another
sample size
the number of indivisuals in the sample; the fraction of the population that youre sampled does not matter, it is the sample size thag determines how well the sample represents the population
bias
systematic deviation from the truth, when the sample does nt relresent the population
voluntary response bias
bias introduced when individuals choose whether or not to participate in the sample; samples based on voluntey reaponse are always invalid and cannot be recovered, no matter how large the sample is
convenience sampling bias
bias introduced when individuals in the sample are convieniently available. these samples fail to represent the population, because every individual in the population is not equally convienient to the sample
undercoverage
some parts of the population are left out and therefore under-represented
non-response
when a large portion of those sampled fail to respond; voluntary response bias is a form of non-response bias
response bias
when something in the survey influences the response of those being sampled; wording of the question or interviewee's behavior can reslut in response bias. poorly worded questions can confuse those responding it
randomizing
protects us from the influences of all the features of our population y making sure that on average the sample looks like the rest of the population
simple random sample (SRS)
a sample of n individuals selected from the population entirely by chance where each member has an equal chance of being selected. every possible sample of size n is equally likely to occur
stratified random sampling
when we first divide the population into strata (homogeneous groups) then choose an SRS within each stratum, and combine the results; it can reduce sampling variability
cluster sampling
when we split the population in similar parts or clusters (heterogeneous groups) and then select one or a fee clusters at random and perfom a census within each one of them
systematic random sampling
...
multistage sampling
...
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