This page calculates the standard error of a sampling distribution of sample means when users input the mean and standard deviation of the population and the sample size.
This page calculates the standard error of a sampling distribution of sample means when users input the mean and standard deviation of the population and the sample size.
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 the difference between two means and displays the probabilities associated with that distribution. Users enter the population standard deviation and the sample sizes, Na and Nb. The applet also calculates the standard error of the sample mean difference.
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.
The page displays the sampling distribution and the standard error of the difference between two sample means. To calculate standard error, enter the standard deviation of the source population, along with the sample sizes, Na and Nb, and then click "Calculate".
Generate a graphic and numerical display of the properties of the F-Distributions, for any value of df_numerator and for values of df_denominator >= 5.
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.
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 chapter of the HyperStat Online Textbook discusses in detail sampling distributions of various statistics (mean, median, proportions, correlation, etc.), differences between such statistics, the Central Limit Theorem, and standard error, giving formulas, examples, and exercises.