A sample size that is too small will not provide reliable answers to the study questions being asked or research hypotheses needing to be tested. A sample size that is too big can make the study unwieldy, wasting both time and efforts. An adequate sample size uses resources and time in the most cost-effective manner and is essential to produce useful research findings.
Statistical power is another fundamental consideration when designing research experiments. Power means the ability of study findings to detect an effect. It goes hand-in-hand with sample size. At a given significance level, the power of the test is increased when having a larger sample size. Generally, the minimum acceptable level is considered to be 80%, which means there are eight out of ten chances of detecting a difference of the specified effect size.
Our statistical experts will help you to develop a simple and comprehensible strategy for sample size and power calculation. To achieve that, the design of sample size and power calculation for the various clinical trials constitutes most of our work:
The significance level, power and magnitude of the difference (effect size) affect the sample size. In simple terms, in a clinical trial or animal study, the size of the difference detected between two groups affects the sample size. If a researcher wants to detect a larger difference, he or she needs to recruit more patients or samples. To avoid the need of extremely large amount of patients or samples, our experts will calculate out a sufficient number of subjects for getting the minimal important difference for your trial. Besides, if there is more than one outcome in your trial, we will choose the largest sample size to make all the outcome measurements fully powered.
The significance level, the effect size and the sample size can affect power. Usually, lower significance level brings out lower power. Our experienced statisticians will conduct an appropriate power calculation to meet your expectation for your trial.
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