An important part of conducting sound science involves interpreting data correctly. Unfortunately, we don’t do that very well. For example, we are fooled by regression to the mean, we report findings when there are none, and we are overconfident about statistical power and significance. As scientists and lay persons, we want to be certain about research findings. But statistics only
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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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As scientists, we have all received some level of training in statistics. A fundamental concept is that we are trying to make inferences about a specific population, but that we only have access to a sample of the people, dogs, amoebas, etc that belong to that population. By randomly sampling amoebas for example, we collect data and conduct statistical tests
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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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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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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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