StatMate

The StatMate application was designed to take the guesswork out of evaluating how many data points you'll need for an experiment, and makes it easy for you to quickly calculate the power of an experiment to detect various hypothetical differences.   Its wizard-based format leads you through the necessary steps to determine the tradeoffs in terms of risks and costs. There is no learning curve with StatMate because it is self-explanatory. All the documentation you need is built right into the program.

Why sample-size matters Many experiments and clinical trials are run with too few subjects. An underpowered study is wasted effort if even substantial treatment effects go undetected. When planning a study, therefore, you need to choose an appropriate sample size.   Your decision depends upon a number of factors including, how scattered you expect your data to be, how willing you are to risk mistakenly finding a difference by chance, and how sure you must be that your study will detect a difference, if it exists.

Some programs ask how much statistical power you desire and how large an effect you are looking for and then tell you what sample size you should use. The problem with this approach is that often you can't really know this in advance.

You want to design a study with very high power to detect very small effects and with a very strict definition of statistical significance. But doing so requires lots of subjects, more than you can afford. StatMate 2 shows you the possibilities and helps you to understand the tradeoffs in terms of risk and cost so you can make sound sample-size and power decisions.

What about power? You also need to know if your completed experiments have enough power. If an analysis results in a "statistically significant" conclusion, it's pretty easy to interpret.   But interpreting "not statistically significant" results is more difficult. Its never possible to prove that a treatment had zero effect, because tiny differences may go undetected. StatMate shows you the power of your experiment to detect various hypothetical differences.