STA 210 Lecture Notes - Lecture 4: Simple Random Sample, Sampling Distribution
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18 Feb 2017
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Confidence Intervals
● Sampling error is also known as margin of error
● The variability seen in a statistic from sample to sample is called sampling variability
● Sampling variability from sample to sample is predictable
● If sample is not a probabilistic sample, then it will be very difficult to do the formal
inference with integrity
● If you were to do the sampling over and over and plot the different statistics you would
get
● The plot is called a sampling distribution
● In particular, it would be bell-shaped and peak above the parameter from the population
● Simple formulas are available for the margin of error and associated confidence intervals,
provided the data were collected in a simple random sample, or similarly statistically
correct fashion
Statistical Sampling
Margin of Error Does Not Apply
● The margin of error is a nice mathematical way of addressing sampling variability, also
called “random sampling error”
● There are lots of other “errors” that can affect data collection and the margin of error
simply doesn’t apply
● Non-Sampling Error- an error or discrepancy caused by something other than the fact
that a sample was selected instead of the entire population
● These errors include; data entry errors, nonresponse, biased questions in a questionnaire,
question order, and false information provided by respondents
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