Significance Testing Principles

  • An online calculator designed to give an estimated sample size that would be needed under specific conditions. This is used only for simple random samples.
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  • If you plan to use inferential statistics (e.g., t-tests, ANOVA, etc.) to analyze your evaluation results, you should first conduct a power analysis to determine what size sample you will need. This page describes what power is as well as what you will need to calculate it.
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  • Calculate the number of respondents needed in a survey using our free sample size calculator. Our calculator shows you the amount of respondents you need to get statistically significant results for a specific population. Discover how many people you need to send a survey invitation to obtain your required sample. You can also calculate the margin of error based on your sample size.

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  • Determining the right sample size in a reliability test is very important. If the sample size is too small, not much information can be obtained from the test in order to draw meaningful conclusions; on the other hand, if it is too large, the information obtained through the tests will be beyond that needed, thus time and money are wasted. This tutorial explains several commonly used approaches for sample size determination.
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  • Presentation that covers: the significance of sample size, determination of sample size, factors that may affect sample size, and how to use sample size in a research or study.
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  • Presentation that applies the topics of power and sample size to examples in epigenetic epidemiology studies. Step by step solutions using statistical softwares G*Power and STATA are given.
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  • The process of sample size calculations, including relevant definitions, is explained and clear examples for different study designs are provided for illustration. A range of software packages and websites are discussed and evaluated
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  • Chapter from a textbook that covers the topic of sample size by giving a thorough background and then covering issues that are involved when determining the sample size.
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  • Resource that gives a clear description of what the p-values and significance levels mean, and what statistical significance means. Graphs are used to illustrate the topics covered in this source.
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  • Resource that covers specific topics within significance testing, including significance levels. P-values and how to determine what qualifies as being statistically significant covered. Examples are given throughout the text to further explain the concepts.
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