Tag Archives: sample size

Does it matter that data are Normally distributed?

Hypothesis testing vs. Estimation Hypothesis tests require that populations are Normally distributed in order for the tests to be reliable. When samples are drawn from Normally distributed populations, the distributions of F or t statistics can be calculated for any given sample size, and the F or t statistic for a specific experiment can be obtained from the distribution. This

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Calculating sample size using precision for planning

Most sample size calculations for independent or paired samples are performed based on power to detect an effect of a certain size, assuming there’s no effect. Instead, Cumming and Calin-Jageman recommend that readers plan studies to detect precise effects. The 95% confidence interval (CI) indicates precision about effects. Therefore, it is possible to plan studies to detect narrow 95% CIs

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Calculating sample size for a paired t-test

Suppose you are planning to conduct a repeated-measures study, where outcomes are measured from the same subject at more than one point in time and the average within-subject effect is calculated using a paired t-test or linear regression. How might you calculate how many subjects need to be tested in order to find an effect? Similar to calculating sample size

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Calculating sample size for a 2 independent sample t-test

Scientists often plan for studies by calculating how many subjects or units need to be tested in order to find an effect. That is, they plan for a study using statistical power according to principles of hypothesis testing. Sample size calculations are usually required in ethics applications and grant proposals to justify the study. We previously learned how to calculate

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Power failure in the neurosciences

There is evidence that many (and possibly most) of the conclusions drawn from biomedical research are probably false (Ioannidis, 2005). In a 2013 Nature Reviews Neuroscience paper entitled “Power failure: why small sample size undermines the reliability of neuroscience”, Button et al. explained how low statistical power is partly to blame for a similar issue in the field of neuroscience.

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