# 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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