G*Power is a necessary tool for every researcher's toolkit
Easy statistical power and sample size calculations
I'm trying to run an online business so I'm fully Google-integrated. I see that there many search queries of different derivations related to sample size calculation as it relates to behind-the-scenes tracking measures.
There is an open-source tool available to EVERYONE that allows you to calculate your own a priori and post hoc power analyses. It is called G*Power and as your personal statistical consultant, I highly suggest you go to the following web address and download Version 3.0 to your respective device:
http://www.gpower.hhu.de/en.html
The researchers that developed this program have made a great contribution to science. It is truly a great and FREE program that can run a litany of different power analyses. You can find out in minutes how large of a sample size that you need, given that you have an idea of the effect size that you are attempting to detect in your study.
Use means, proportions, and variance measures from published studies in your field to have the most empirically rigorous hypothesized effect. Enter these values into G*Power and the adjust the variance and magnitude of the effect size to see how the required sample size changes.
Click on the Sample Size button to access the methods of conducting and interpreting sample size calculations for ten different statistical tests.
There is an open-source tool available to EVERYONE that allows you to calculate your own a priori and post hoc power analyses. It is called G*Power and as your personal statistical consultant, I highly suggest you go to the following web address and download Version 3.0 to your respective device:
http://www.gpower.hhu.de/en.html
The researchers that developed this program have made a great contribution to science. It is truly a great and FREE program that can run a litany of different power analyses. You can find out in minutes how large of a sample size that you need, given that you have an idea of the effect size that you are attempting to detect in your study.
Use means, proportions, and variance measures from published studies in your field to have the most empirically rigorous hypothesized effect. Enter these values into G*Power and the adjust the variance and magnitude of the effect size to see how the required sample size changes.
Click on the Sample Size button to access the methods of conducting and interpreting sample size calculations for ten different statistical tests.
