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Learn the differences between parameters, samples, and populations in statistical research. Discover the importance of random sampling and how statistics help estimate population characteristics.
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Suppose the Office of Institutional Research and Planning (IRP) calls 50 recent alumni and ask about their current income. The average reported monthly income of these 50 alumni was $3,100.
Question: The 50 surveyed students are a_____. • Parameter • Population • Sample • Statistic
Question: “average income of $3,100” is a _______? • Parameter • Population • Sample • Statistic
What would need to change with regards to the sample size, n, to make the “reported average income” a parameter? • n=1 • n=1,000 • n=Number of recent alumni=N • You can’t find the parameter value because the population is infinite in size.
A parameter describes a characteristic of the population. • We typically can’t sample the entire population (do a census), so we collect a sample. • We design our sample so it is likely representative of the population. • A statistic is calculated from a sample. We use the statistic to estimate the parameter.
Very first steps • Define your population • Determine what parameter of the population is of interest to you. (What’s the study’s question?)
A prospective cohort study of over 1,300 children found that children who are breast-fed for a year, on average, test 4 IQ points higher than non breast-fed children. (JAMA 7/29/2013)
Question: Not knowing more details about this study, this study could most likely be classified as an____________. • observational study. • experiment.
Question: To use simple random sampling to sample 10 people from this class of 80, I should: • Use every 8th name from my roll sheet. • Have people write their names on a piece of paper, put them in a hat, and pull 10 pieces of paper from the hat. • Ask for 10 volunteers. • Assign each person a number (1-80) and then use a computer or random number table to get 10 names.
Why use randomness when sampling? Avoid bias!