Laboratories

  • This resource defines and explains the median using an example on employee salaries.
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  • This resource defines and explains percent changes using an example on city murder rates.
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  • This resource defines and explains per capita rates using an example on city murder rates.
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  • This resource explains margin of error using an example on presidential popularity polls.
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  • This resource gives 3 questions readers should ask when presented with data and why to ask them: Where did the data come from? Have the data been peer-reviewed? How were the data collected? This page also describes why readers should: be skeptical when dealing with comparisons, and be aware of numbers taken out of context.

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  • This resource discusses sample sizes and how they are chosen.
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  • This resource explains the t-distribution and hypothesis testing (informally) using an example on laptop quality.
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  • This applet allows students to explore three methods for measuring "goodness of fit" of a linear model. Users can manipulate both the data and the regression line to see changes in the square error, the absolute error, and the shortest distance from the data point to the regression line.
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  • This lesson poses a series of questions designed to challenge students' possible misconceptions of statistical inference and hypothesis testing. The lesson uses the statistical software, Fathom, and three datasets with information on the number of chips per canister distributed by a snack maker. The data can found at the relation address below.
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  • This page provides a table for selecting an appropriate statistical method based on type of data and what information is desired from the data. It also compares parametric and nonparametric tests, one-sided and two-sided p-values, paired and unpaired tests, Fisher's test and the Chi-square test, and regression and correlation. It comes from Chapter 37 of the textbook, "Intuitive Biostatistics".
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