Statistical Inference & Techniques

  • 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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  • Video that explains what p-values and significance levels are in hypothesis testing.
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  • Video that will teach you how to interpret the P-Value and significance level for a two-tailed hypothesis test that is not rejected.
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  • Resource that covers what significance testing is and what it is used for. Describes the significance level and how it relates to the p-value with respect to hypothesis testing. A glossary of key concepts and terms is included at the end of the document.
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