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# Univariate Distributions

• ### Margin of Error

This resource explains margin of error using an example on presidential popularity polls.
• ### Data Analysis

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.

• ### Sample Sizes

This resource discusses sample sizes and how they are chosen.
• ### Dataset Example: Student's T

This resource explains the t-distribution and hypothesis testing (informally) using an example on laptop quality.
• ### Quote: Gibbon on Probability

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

• ### Analysis Tool: Two Arm Binomial

This page calculates either estimates of sample size or power for differences in proportions. The program allows for unequal sample size allocation between the two groups.

• ### Analysis Tool: Distributions JAVA Applet

This applet displays various distributions and allows the user to experiment with the parameters to see the effects on the curve.

• ### Joke: statistics terminology

Joke from "The Little Black Book of Business Statistics", by Michael C. Thomsett (1990, Amacom) p. 117. also quoted in "Statistically Speaking" compiled by Carl Gaither and Alma Cavazos-Gaither.
• ### Statistical Literacy Curriculum Design

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.