StudySize is 
              a user friendly Windows application to calculate the sample size 
              needed in a study, survey, or experiment. It 
              can be used to study a range of scenarios when the plan is to test, 
              give an estimate, or calculate a confidence interval for the parameter 
              of interest.
             It comprises 
              a wide range of procedures with examples and explanations of the parameters in a help menu. There 
              is also a separate pdf document with the examples for printout. 
              The results are presented as tables or graphs and summarized in 
              text to be copied and pasted into slides or other documents.
            The calculations 
              in StudySize have been validated against the articles they are based 
              on as well as against other sample size software.
             
            Summary 
            of features. 
         
          
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Calculations 
                for most standard tests as well as for specific parameters in 
                common statistical distributions 
             
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Comprises 
                hypothesis testing, point estimation, and confidence interval
             
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Any 
                parameter can be calculated (not only sample size and power) 
             
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Simultaneous 
                power comparisons in multiple windows
             
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Results 
                presented as single values or in tables and graphs grouped by 
                two other parameters
             
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Text 
                summary and description of performed analysis
             
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Probability-, 
                distribution-, and hazard function plots for common statistical 
                distributions.
             
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Monte 
                Carlo simulation option when large sample formulas may be inappropriate
             
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Equivalence- 
                and non-inferiority testing for most tests
             
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Group 
                sequential methods for the most common designs
             
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Multiple 
                comparisons using the most common approaches
             
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A 
                wide range of non-parametric tests for different assumed statistical 
                distributions