STA 210 Lecture Notes - Lecture 4: Simple Random Sample, Sampling Distribution

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18 Feb 2017
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
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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