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The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power .
Matched or independent study designs may be used. Power, sample size, and the detectable alternative hypothesis are interrelated. The user specifies any two of these three quantities and the program derives the third. A description of each calculation, written in English, is generated and may be copied into the user's documents.
PASS is a computer program for estimating sample size or determining the power of a statistical test or confidence interval. NCSS LLC is the company that produces PASS. NCSS LLC also produces NCSS (for statistical analysis). PASS includes over 920 documented sample size and power procedures.
WebPower Free online statistical power analysis (https://webpower.psychstat.org) Free and open source online calculators (https://powerandsamplesize.com) PowerUp! provides convenient excel-based functions to determine minimum detectable effect size and minimum required sample size for various experimental and quasi-experimental designs.
The software also includes reference interval estimation, [9] meta-analysis and sample size calculations. The first DOS version of MedCalc was released in April 1993 and the first version for Windows was available in November 1996.
G*Power is a free-to use software used to calculate statistical power. The program offers the ability to calculate power for a wide variety of statistical tests including t-tests, F-tests, and chi-square-tests, among others.
OpenEpi is a free, web-based, open source, operating system-independent series of programs for use in epidemiology, biostatistics, public health, and medicine, providing a number of epidemiologic and statistical tools for summary data.
Set up two statistical hypotheses, H1 and H2, and decide about α, β, and sample size before the experiment, based on subjective cost-benefit considerations. These define a rejection region for each hypothesis. 2 Report the exact level of significance (e.g. p = 0.051 or p = 0.049). Do not refer to "accepting" or "rejecting" hypotheses.