MKT 352 2nd Exam:
Population: the entire group under study as defined by research objectives.
Census: an accounting of the complete population.
Sample Unit: the individual “things” that make up the population. It is usually the basic level of
investigation.
Sampling Frame: A way to identify population members, often a physical list of sample “units,”
but sometimes being a method of selection not requiring an actual list.
Random Sampling Error: due to the random chance of getting sample members that are unusual
in regard to the parameter being estimated.
4 Reasons for taking a sample instead of a census: 1. Faster 2. More accurate 3. May not be
possible 4. Cheaper.
Probability Sampling: those is which members of the population have a known chance of being
selected into the sample.
Non-Probability Samples: those where the chances of selecting members from the population
into the sample are unknown.
Simple Random Sampling: the probability of being selected into the sample is equal for all
members of the population.
What are 2 Methods for drawing a simple random sample from a sampling frame: A: 1. Random
Device Method 2. Random Numbers Method.
Systematic Sampling:Ttype of random sampling done for ease in selecting names.
Cluster Sampling: type of random sampling where people within a cluster need to be
heterogeneous and be a small model of the population; done for closest efficiency.
Stratified Sampling: type of random sampling where people within a cluster need to be
homogeneous; done for greater accuracy.
Non-Probability Sampling: type of sampling method where some members of the population do
not have any chance of being included in the sample.
What is the main problem with non-probability sampling: A: There isn’t a way to judge the
accuracy.
Convinence Sample: samples drawn at the convenience of the interviewer.
Judgement Sample: sample drawn that require an '"educated guess" as to who should represent
the population.
Confidence Level: the probability that an interval around the sample statistic which is defined by
the confidence interval contains the true population value.
Standard Error: tells us that the values of a statistic when taken from all of the many possible
samples which could be drawn from any population will have an average value which is equal to
that of the population. All of the possible sample statistic values will be normally distributed
around this true mean value. The standard deviation of a statistic such as a mean from this
"derived population" is known as the standard error.
Why would doubling the size of a sample NOT cut the size of the sampling error in half? A:
Because of the nature of the curved relationship between sample size and sample error.
Why is it a lousy idea to say that a sample, which is a certain percentage of the population, is
adequate? In almost all cases, the accuracy of a random sample is independent of the size of the
population.
Null Hypothesis: hypothesis that the difference in their population parameters is equal to zero
Directional hypothesis: indicates the direction in which you believe the population parameter
falls relative to some target mean or percentage
