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Introduction
Let's get started! Here is what you will learn in this lesson.
Learning objectives for this lesson
Upon completion of this lesson, you should be able to do the following:
Recognize and distinguish between various sampling schemes
Understand the importance of random sampling as it applies to the study of statistics
Designing Samples
Then entire group of individuals about which information is wanted is called the
populations
. It ma be somewhat abstract. The part of the population actually
examined to gather information is the
sample
. It is more concrete and immediate than the population.
Example
Identify the population and the sample in the following:
A survey is carried out at a university to estimate the proportion of undergraduates living at home during the current term.
Population
: all undergraduates
at the university;
Sample
: those undergraduates surveyed.
1.
In 2005, investigators chose 400 teachers at random from the National Science Teachers Association list and polled them as to whether or not they believed
in biblical creation (hypothetical scenario). 200 hundred of the teachers sampled responded.
Population
: National Science Teachers Association members;
Sample
: the 200 respondents.
2.
A balanced coin is flipped 500 times and the number of heads is recorded.
Population
: all coin flips;
Sample
: the 500 coin flips.
3.
Any sample taken should be selected at random; otherwise it may be prone to bias. Bias is the systematic favoring of certain outcomes. For example, people who
volunteer for a treatment may bias results toward conclusions that the treatment offers an improvement over a current treatment. A sample should be selected
using some probability sampling design which gives each individual or participant a chance of being selected. Four common probability sampling schemes are:
Simple Random Sampling (SRS)
– a simple random sample of size N consists of N individuals from the population chosen in such a way that every set of
N individuals has an equal chance of being selected.
1.
Stratified Random Sampling
– The population is divided into important subgroups (e.g. East and West; Freshmen, Sophomore, Junior, Senior) which are
groups of individuals or subjects that are similar in a way that may affect their response – think of stratifying a university’s undergraduate population by
race, gender, or nationality. Then separate simple random samples are taken from each subgroup. These subgroups are called strata. This is done to be sure
every important subgroup is represented properly in the overall sample which will enhance the efficiency of this design.
2.
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 Fall '08
 BARROSO,JOAOR
 Statistics

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