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Continuous

  • 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 day may possibly be my last: but the laws of probability, so true in general, so fallacious in particular, still allow about fifteen years. A quote of English historian Edward Gibbon (1737 - 1794). The quote was written in 1787 and was published after his death in "Miscellaneous works of Edward Gibbon, with memoirs of his life and writings composed by himself" edited by Lord John Seffield, 1796
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  • A specially-designed statistical literacy course is needed for college students in majors that don't require statistics or mathematics. This paper suggests that key topics in conditional probability, multivariate regression and the vulnerability of statistical significance to confounding should be included and presents some new ways to teach these ideas.
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  • This PowerPoint lecture presentation discusses comparing the means of two dependent populations using the paired T-test and defines the concepts of this hypothesis test. The original presentation is available for downloading.
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  • This short article discusses the difference between "important" and "statistically significant." The data used come from a study comparing male faculty salaries to female faculty salaries.
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  • This webpage uses the criminal trials in the US Justice system to illustrate hypothesis testing, type I error, and type II error. An applet allows the user to examine the probability of type I errors and type II errors under various conditions. An applet allows users to visualize p-values and the power of a test. Keywords: type I error, type II error, type one error, type two error, type 1 error, type 2 error
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  • This video is an example of what is known in psychology as selective attention. When a person is instructed to only focus on the number of times a ball is passed between players wearing a white shirt it is sometimes difficult to see what else is going on.
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