Call to Action
Want to know how to find the right sample size for your study? Too few subjects and you’ll waste time. Too many subjects and you’ll waste time and money! Learning the concepts of statistical power analysis and how to put the power analysis techniques to use will help to assure that you’ll collect a large enough sample to see significance in the effect you test.
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an exploration of power analysis and techniques for determining sample size (a priori techniques).
Elaine will take some time during the event to work examples in the free to use G*Power software. She will also present some code and demonstrations for powering studies and performing power analysis simulations in R software.
Why Should You Attend:
The power of your study is the probability that you will find a statistically significant difference or relationship (an “effect”) if that difference or relationship (effect) truly exists in the population.
A study with too small of a sample size is under-powered. This means that even if the effect you are testing for truly exists, you won’t achieve statistical significance. You will waste time by collecting a sample that is too small to properly power a study. Why perform a research if you can’t see significance for your desired effect?
A study with too large of a sample is over-powered. This means that you’ve collected such a large sample that you will see significance even on very small effects. However, the costs of subject recruitment, data collection, and follow-up (if needed) are quite large. Recruiting more subjects than needed unnecessarily inflates the temporal and monetary costs.
Questions related to the feasibility of a study can be answered by power analysis:
Answers to questions like these will give you an idea if your study is indeed “do-able.”
Areas Covered in the Webinar:
Who Will Benefit:
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