This page performs a Kolmogorov-Smirnov "Goodness of Fit" test for categorical data. Users enter observed frequencies and expected frequencies for up to 8 mutually exclusive categories. The applet returns the critical values for the .05 and .01 levels of significance.
Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.
This page generates a graph of the Chi-Square distribution and displays the associated probabilities. Users enter the degrees of freedom (between 1 and 20, inclusive) upon opening the page.
This applet generates a graph of the sampling distribution of sample means and displays the probabilities associated with that distribution. Users enter the mean and standard deviation of the source population and the size of the samples. The applet also calculates the standard error of the sample means.
This page generates a graph of the sampling distribution of r, the Pearson correlation coefficient. Upon opening, the applet prompts for sample size greater than 6. The applet also displays the probabilities associated with the distribution.
Calculates the areas under the curve of the normal distribution falling to the left of -z, to the right of +z, and between -z and +z.
Given the population incidence of a certain disease, and the conditional probabilities of positive and negative test results, what are the probabilities for a particular test result of a true positive, true negative, false positive, and false negative? Adaptable to other kinds of conditional situations. Although this page is adaptable to a variety of backward probability situations, its exemplary case is the one in which one is seeking to make sense of the result of a medical test.
This page will generate a graphic and numerical display of the properties of a binomial sampling distribution, for any values of p and q, and for values of n between 1 and 40, inclusive.
Log-linear analysis is a version of chi-square analysis in which the relevant values are calculated by way of weighted natural logarithms. This page will calculate several values of G^2.
In the first simulation, random samples of size n are drawn from the population one sample at a time. With df=3, the critical value of chi-square for significance at or beyond the 0.05 level is 7.815; hence, any calculated value of chi-square equal to or greater than 7.815 is recorded as "significant," while any value smaller than that is noted as "non-significant." The second simulation does the same thing, except that it draws random samples 100 at a time. The Power of the Chi-Square "Goodness of Fit" Test pertains to the questionable common practice of accepting the null hypothesis upon failing to find a significant result in a one- dimensional chi-square test.