Effect Size

  • November 24, 2009 Activity webinar presented by Carl Lee, Central Michigan University, and hosted by Leigh Slauson, Capital University. This webinar introduces a real-time online hands-on activity database for teaching introductory statistics. One particular activity, "How well can hand size predict height?", is used to engage students with a real-time activity in order to learn bivariate relationships. Various other activities can be found at stat.cst.cmich.edu/statact. The real-time database approach speeds up the process of data gathering and shifts the focus in order to engage students in the process of data production and statistical investigation.
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  • In this game activity, students match correlation values with plots generated by the applet. Competition in this game setting encourages students to become more involved in the classroom and attainment of learning objectives. This game is best if used in a lab setting, although it may be modified to fit other classroom situations.
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  • This activity begins with an instructor demonstration followed by a student out-of-class assignment. Students will observe their instructor create a scatterplot and observe how the correlation coefficient changes when outlier points are added. Students are then given a follow up assignment, which guides them through the applet. In addition, the assignment provides insight about outliers and their effect on correlation. This activity will show exactly how outliers numerically change the correlation coefficient value and to what degree.
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  • This visualization activity combines student data collection with the use of an applet to enhance the understanding of the distributions of slope and intercept in simple linear regression models. The applet simulates a linear regression plot and the corresponding intercept and slope histograms. The program allows the user to change settings such as slope, standard deviation, sample size, and more. Students will then see theoretical distributions of the slope and intercept and how they compare to the histograms generated by the simulated linear regression lines.
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  • This in-class demonstration combines real world data collection with the use of the applet to enhance the understanding of sampling distribution. Students will work in groups to determine the average date of their 30 coins. In turn, they will report their mean to the instructor, who will record these. The instructor can then create a histogram based on their sample means and explain that they have created a sampling distribution. Afterwards, the applet can be used to demonstrate properties of the sampling distribution. The idea here is that students will remember what they physically did to create the histogram and, therefore, have a better understanding of sampling distributions.
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  • A joke about the need for students to explain how they arrived at the answers they provide on exams.

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  • This text article gives a relatively short description of the concept of p-values and statistical significance. This article aimed at health professionals frames the idea of statistical significance in the setting of a weight loss program. In addition to discussing p-values and comparing them with confidence intervals, the article touches on the ideas of practical significance and the fact that the significance of 0.05 is arbitrary.
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  • 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

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  • The applets in this section of Statistical Java address Power. Users can perform one or two tailed tests for proportions or means for one or two samples. Set the parameters and drag the mouse across the graph to see how effect size affects power. An article and an alternative source for this applet can be found at http://www.amstat.org/publications/jse/v11n3/java/power/ This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Power.html
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  • This site contains materials to help teach a Chance course. It includes a newsletter, videos and audios, teaching aids, and other related Internet sources.
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