When preparing to conduct a trial, you will want to make sure that the experiment has sufficient statistical power. In other words, you want some confidence that you are likely to find the effect you are looking for. As outlined on the power page, there are several factors that impact the power of an analysis. Often, the only factor under your direct control is the sample size (i.e. number of subjects in the trial). Since larger trials take more time and resources than smaller trials, you probably want to determine the minimum sample size necessary to achieve an acceptable level of statistical power.
In order to estimate the necessary sample size, we need to know the effect size in advance. This is a chicken and egg problem: how can we know the effect size before we've conducted the study? There are two strategies available.
One approach is to use another data set to predict the likely effect size. For example, you may conduct a small pilot study to obtain a rough estimate. Alternatively, you can use the results from a related study, such as one published by another team conducting research on a similar topic.
A second approach is to use clinical judgment to specify the smallest effect size that you consider to be relevant. For example, if you feel that it is important to detect even small effects, you may select a value of 0.2 (see this page for a rough categorization of effect size levels).
This calculator tells you the minimum number of participants necessary to achieve a given power. The following parameters must be set: