In this applet, we simulate a series of hypothesis of tests for the value of the parameter p in a Bernoulli random variable. Each column of red and green marks represents a sample of 30 observations. "Successes'' are coded by green marks and "failures'' by red marks.
This online, interactive lesson on hypothesis testing provides examples, exercises, and applets which includes tests in the normal model, Bernoulli Model, and two-sample normal model as well as likelihood ratio and goodness of fit tests.
This collection of calculators allows users to perform a number of statistical applications. Each provides background on the procedure and an example. Users can compute Descriptive Statistics and perform t-tests, Chi-square tests, Kolmogorov-Smirnov tests, Fisher's Exact Test, contingency tables, ANOVA, and regression.
This page calculates either sample size or power for a one sample binomial problem. Users choose between a one-sided and two-sided test and specify the null and alternative hypothesized proportions. The calculator also gives the critical value.
This page calculates either estimates of sample size or power for differences in proportions. The program allows for unequal sample size allocation between the two groups.
This page calculates probabilities for a Poisson distribution.
This random number service allows users to generate up to 10,000 random integers with duplicates, randomized sequences without duplicates, or up to 16 kilobytes of raw random bytes. Users can also flip virtual coins and generate random bitmaps. Key word: Random Number Generator.
This calculator computes the chi-square statistic, degrees of freedom (DoF), and p-value for the Chi-square test for equality of distributions. Users input a table of values with row and column labels without total scores. The null hypothesis is that the all the samples have the same distribution.
This test checks whether an observed distribution differs from an expected distribution. It computes the chi-square statistic, degrees of freedom (DoF), and p-value. Users input a table with row and column labels, observed frequencies on the first row, and expected frequencies on the second row. The null hypothesis is that the observed values have the expected frequency distribution.
This page introduces contigency tables with an example on fruit trees and fire blight. Two calculators are provided that allow users to enter their own contigency table and test for treatment effects. The first calculator performs Fisher's Exact Test on a 2x2 tables. The second performs a chi-square test on up to a 9x9 table.