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 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 applet simulates drawing samples from a binomial distribution. Users set the population proportion of success (pi), sample size (n), and number of samples. By clicking "Draw Samples," the applet will draw a sample and display the corresponding sample histogram. Each new sample drawn is added to the previous ones unless the user clicks "Reset" between samples. Users can choose to display the number and proportion of successes above or below a certain value (tail probabilities) by entering a value in the "Num Successes" box and clicking "Count." The portion of the distribution that meets the condition is highlighted in red, and the proportion of success is given at the bottom of the page. Clicking the inequality sign changes its direction. Clicking "Theo Values" displays the theoretical distribution in green on top of the empirical. Instructions and an activity for this applet can be found in the textbook "Investigating Statistical Concepts, Applications, and Methods" (ISCAM) in Lesson 3.2.2 on page 205.
Everyday we have specific routines we engage in. Many of these routines are tailored to preventing us from becoming victims of crime. We do things like lock our doors, watch where we walk at night, or avoid walking alone. We take these actions because at some level we are afraid of the possibility of being a victim of crime. Although we may not consciously think about it, these routines may be influenced by a variety of factors. What factors might make some individuals more afraid than others?
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 provides a table of F distribution probabilities for alpha = 0.10, 0.05, 0.025, and 0.01.
This page provides a z-table with alpha levels from .00 to .09.
This page provides a t-table with degrees of freedom 1-30, 60, 120, and infinity and seven levels of alpha from .1 to .0005.
Compared to probability calculators, the traditional format of distribution tables has the advantage of showing many values simultaneously and, thus, enables the user to examine and quickly explore ranges of probabilities. This webpage includes a list of distributions and tables, including the standard normal (Z) table, student's t table, chi-square table, and F distribution tables. An animation of the density function and distribution function is shown above each distribution table to demonstrate the effects changing degrees of freedom and significance levels have on the shape of a distribution.