Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.
Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.
This applet demonstrates the concept of power. Users select the hypothesized mean, the alternative mean, the sample size, and the number of samples. The applet shows the hypothesized histogram and the alternative histogram. Users then select either the level of significance and set alpha or the rejection region and set the test statistic. The applet then shows the p-value (in red) and power (in green). User can also determine the direction of the test by clicking the inequality sign.
(Uses JAVA) Some basic statistical analysis tools that allow the user to input their own data or use the pre-existing data and perform the desired test (e.g ANOVA, Descriptive, t-test, chi-square, correlation and regression).
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.
When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.