Significance Testing Principles

  • This is a complete lesson module (including example problems with answers to selected problems) for the purpose of enabling students to: 1) Provide examples demonstrating how the margin of error, effect size, and variability of the outcome affect sample size computations. 2) Compute the sample size required to estimate population parameters with precision. 3) Interpret statistical power in tests of hypothesis. 4) Compute the sample size required to ensure high power when hypothesis testing.
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  • When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.

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  • Document (pdf) illustrating a test of normality using an Anderson-Darling test in MINITAB and a test of equality of variances with an F-test in EXCEL.
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  • Powerpoint explaining what power is and how power and sample size are related to one another.
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  • A resource providing information about what the sample size is, what factors the sample size depends on, and how it can be determined,
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  • Resource providing information about: computation of the sample size and the assumptions that must be made to do so. Several examples are given with different conditions in each, and a table showing minimum sample sizes for a two-sided test.
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  • Article that explains why comparing statistical significance, sample size and expected effects are important before constructing and experiment.
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  • This is an online calculator that can be used to determine the recommended sample size that is needed for a specific margin of error, confidence level, and population size.

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  • 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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