For example, you hypothesize that an eye drop treatment can reduce dry eye symptoms by 10% in your test group. One of the most common scenarios is comparing two different proportions. Sample CalculationsĬomparing 2 proportions: 2-Sample, 2-Sided Equality
#STATISTICAL CALCULATIONS FOR TRIALS FREE#
Fortunately, there are various free online calculators ( ) that will help you calculate your optimal sample size based on a few key input variables. If it’s too large, then the study becomes too expensive. If it’s too small, then you won’t see an effect. The aim, however, is to select a reasonable sample size. Overall, an increase in sample size will increase the probability that your test will be able to detect a small effect size. It is also commonly denoted as (1- β), where β refers to Type II error (false negative). Power analysis (or statistical power) determines the probability that a test will observe an effect. Unlike conventional statistical tests that analyze collected data, we need a predictive statistical tool that can be used before any data is even collected. This article will guide you through the basics of calculating the optimal sample size for your clinical study. It can be quite an intimidating process if you have not done it before but don’t worry. After all, selecting the wrong size can be disastrous for a study later on. Sample size is one of the most important criteria when it comes to designing an investigator initiated trial.
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