Search results
Results From The WOW.Com Content Network
Use this advanced sample size calculator to calculate the sample size required for a one-sample statistic, or for differences between two proportions or means (two independent samples). More than two groups supported for binomial data. Calculate power given sample size, alpha, and the minimum detectable effect (MDE, minimum effect of interest).
Free, Online, Easy-to-Use Power and Sample Size Calculators. no java applets, plugins, registration, or downloads ... just free. Go Straight to the Calculators ».
The power calculator computes the test power based on the sample size and draw an accurate power analysis chart. Larger sample size increases the statistical power. The test power is the probability to reject the null assumption, H 0, when it is not correct. Power = 1- β.
This calculator uses a variety of equations to calculate the statistical power of a study after the study has been conducted. 1 "Power" is the ability of a trial to detect a difference between two different groups.
Choose which calculation you desire, enter the relevant population values for mu1 (mean of population 1), mu2 (mean of population 2), and sigma (common standard deviation) and, if calculating power, a sample size (assumed the same for each sample).
Power analysis combines statistical analysis, subject-area knowledge, and your requirements to help you derive the optimal sample size for your study. Statistical power in a hypothesis test is the probability that the test will detect an effect that actually exists.
G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.
Calculate test power for z-test and t-test, one sample or two samples and draw an accurate power analysis chart.
Interactive calculator for illustrating power of a statistical hypothesis test.
Calculate sample size of a certain power for your experiments. Baseline Conversion Rate (%) Minimum Detectable Effect (%) Hypothesis. One-sided Test (Recommended) Used to determine if the test variation is better than the control (Recommended) Two-sided Test.