GraphPad StatMate versions & prices


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GraphPad StatMate is an extremely easy-to-use software for the determination of sample size for laboratory and preclinical studies. With GraphPad StatMate you are not guessing sample sizes anymore. You can easily and quickly calculate the power for an experiment. A wizard guides you through the necessary steps. StatMate is self-explanatory and all the documentation you need is integrated in the software.

Arguments for GraphPad StatMate:

  • Wizard based software
  • Understandable without in depth knowledge of statistics
  • Simple calculation of the sample size for different study designs
  • Report for power analysis is created automatically

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Graphpad StatMate

GraphPad StatMate calculates sample size, power and more. A perfect companion to InStat or Prism.

StatMate: Your sample size and power wizard

GraphPad StatMate takes 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.

StatMate shows you the tradeoffs

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.

statmate screen statmate screen

Further Information

  Windows Mac
Further Requirements    
Operating System Windows 98, XP, Vista, 7) Mac OS 9 or OS X 10.3 - 10.6
StatMate will not run under OS X 10.7+
Minimum CPU    
Min. RAM    
Disk Space 2 MB 4 MB

Calculate sample size - How many subjects (data points) do you need? Naturally, the answer is "it depends". It depends on how large a difference you are looking for, how much your data vary, and on how willing you are to risk mistakenly finding a difference by chance or mistakenly missing a real difference. StatMate helps you see the tradeoffs, so you can pick an appropriate sample size for your experiment.

Calculate power - Just because a study reaches a conclusion that the results "are not statistically significant" doesn't mean that the treatment was ineffective. It is possible that the study missed a small effect due to small sample size and/or large scatter. StatMate calculates the power of a test to detect various hypothetical differences.

Choose from these experimental designs:

  • Compare two means (unpaired t test)
  • Compare two paired means (paired t test)
  • Compare two survival curves (logrank test)
  • Compare two proportions (chi-square test)
  • Compare a mean with a hypothetical value (one-sample t test)