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  • This tutorial introduces 9 sources of threat to internal vailidity and asks the user to classify hypothetical experiments as either internally valid or invalid and identify the source of threat if invalid.
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  • The t-distribution activity is a student-based in-class activity to illustrate the conceptual reason for the t-distribution. Students use TI-83/84 calculators to conduct a simulation of random samples. The students calculate standard scores with both the population standard deviation and the sample standard deviation. The resulting values are pooled over the entire class to give the simulation a reasonable number of iterations.
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  • These lecture notes are composed of nearly 180 PowerPoint slides that have been coverted to a pdf file (6 per page) on Biomedical Imaging. The following topics are outlined: Vocabulary, Displaying Data, Central Tendency and Variability, Normal Z-scores, Standardized Distribution, Probability, Samples & Sampling Error, Type I and Type II Errors, Power of a Test, Hypothesis Testing, One Sample Tests, Two Independent Sample Tests, Two Dependent Sample Tests & Estimation, Correlation and Regression Techniques, Non-Parametric Statistical Tests, Applications of Central Limit Theorem, Law of Large Numbers, Design of Studies and Experiments, Fisher's F-Test, Analysis Of Variance(ANOVA), Principle Component Analysis (PCA), Chi-Square Goodness-of-fit test, Multiple Linear Regression, General Linear Model, Bootstrapping and Resampling.
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  • This resource defines what a p-value is, why .05 is significant, and when to use it. It also covers related topics such as one-tailed/two-tailed tests and hypothesis testing.
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  • The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples. A two-sample t-test (Pooled and Satterthwaite) is performed for k = 2. An ANOVA test is performed for k > 2. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Data.html
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  • In this activity, students work in groups to provide practical interpretations of graphs, considering shape, center, and spread. Each group posts their interpretation for one graph and critiques other groups' interpretations on other graphs. Students examine key aspects (shape, spread, location, etc) of histograms and stem plots to develop the ability to interpret graphics. This activity gets the students up and out of their seats and working together. It is a good activity for early in a term. The Gallery Walk idea can be adapted for different sized classes but this activity has been designed for classes up to 65 students.
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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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  • The applets in this section of Statistical Java allow you to see how levels of confidence are achieved through repeated sampling. The confidence intervals are related to the probability of successes in a Binomial experiment. The main page gives the equation for finding confidence intervals and describes the parameters (p, n, alpha). Each applet allows you to change a different parameter and simulate sampling to demonstrate the long run proportion of intervals that contain the true probability of success. The applets are available from a pull-down menu at the bottom of the page. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/CI.html
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  • This resource defines and explains binomial probability, including examples and exercises for the learner.
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  • This resource includes problem-based teaching and learning materials for statistics that are based around specific problems arising in biology, business, geography and psychology. The STEPS modules are intended to be used as problem-based lab material that may support existing coursework.
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