A joke about the need for students to explain how they arrived at the answers they provide on exams.
A joke about the need for students to explain how they arrived at the answers they provide on exams.
Statistically Speaking is a 5 minute 35 second video that can be used in discussing various concepts in descriptive statistics. The video was written, directed, and produced by Cameron W. Hatch and the cast includes (order of appearance) Mala Grewal, Sally Atkinson, Griffin Hatch, Jeff Hatch, Matt Burnham, and Sylvia Burnham.
In the fields of observation chance favors only the prepared mind. A quote from French chemist and microbiologist Louis Pasteur (1822 - 1895) given at a lecture at University of Lille on December 7, 1854.
Song about the properties of Maximum Likelihood Estimation including efficiency, invariance, and asymptotic normality. May sing to the tune of "Let it Be" By Paul McCartney. Recorded June 26, 2009 at the OSU Whisper Room: Larry Lesser, vocals/guitar; Justin Slauson, engineer.
Song about the use of the 5-number summary to describe skewed data as an alternative to the mean and standard deviation. May be sung to the tune of the 1979 song "I Will Survive" by Gloria Gaynor. Lyrics written by Sheila O'Leary Weaver. The song took first place in the song category in the 2007 A-Mu-Sing competition. Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.
Song about the chi-square goodness-of-fit test. May be sung to the tune of James Rado, Gerome Ragni, and Gai MacDermot's 1969 song "Aquarius" from the musical "Hair." Lyrics by Lawrence Mark Lesser. Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.
Measures of the size of an effect based on the degree of overlap between groups usually involve calculating the proportion of the variance that can be explained by differences between groups. This resource outlines different approaches to measuring this proportion.
This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test. They carry out further simulations to estimate the power of the test. After pooling their data with that of other groups, they construct a power curve. A theoretical power curve is also constructed, and the students discuss why there are differences between the expected and estimated curves. Key words: Power, size, hypothesis testing, binomial distribution
This handout lists the most commonly used effect sizes, adjustments, and rules of thumb concerning sample size calculation.
G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, ztests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.