Significance Testing Principles

  • This site gives an explanation, a definition of, and an example using comparison of two means. Topics include confidence intervals and significance tests, z and t statistics, and pooled t procedures.
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  • This online, interactive lesson on expected value provides examples, exercises, and applets in which students will explore relationships between the expected value of real-valued random variables and the center of the distribution. Students will also examine how expected values can be used to measure spread and correlation.
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  • This online, interactive lesson on special distributions provides examples, exercises, and applets covering normal, gamma, chi-square, student t, F, bivariate normal, multivariate normal, beta, weibull, zeta, pareto, logistic, lognormal, and extreme value distributions.
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  • This site offers a collection of applets in which standard topics of statistics and probability are presented in a novel and visual way using computer animated images. Topics include dependence, independence, conditional probabilities, expectation and variance, normal, exponential, Poisson distributions, law of large numbers and the central limit theorem, hypothesis testing maximum likelihood estimation, sampling, chi-square tests, and the construction of confidence intervals.
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  • This applet is a probabilistic study of picking fortunes from a limited supply of fortune cookies. The student will try to answer how many cookies he/she has to eat to have a 50/50 chance of reading all the fortunes.
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  • This site explains small sample hypothesis testing for a normal population and hypothesis testing for a population proportion. Includes examples and exercises.
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  • This site focuses on using the LRT to compare two competing models.
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  • Using the Fisher r-to-z transformation, this page will calculate a value of z that can be applied to assess the significance of the difference between two correlation coefficients, r_a and r_b, found in two independent samples. If r_a is greater than r_b, the resulting value of z will have a positive sign; if r_a is smaller than r_b, the sign of z will be negative.

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  • For a table of frequency data cross-classified according to two categorical variables, X and Y, each of which has two levels or subcategories, this page will calculate the Phi coefficient of association; perform a chi-square test of association, if the sample size is not too small; and perform the Fisher exact probability test, if the sample size is not too large. For intermediate values of n, the chi-square and Fisher tests will both be performed.

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  • This page will compute the t-test for either correlated or independent samples. One may copy and paste data in or type the data in individually.

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