Tag Archives: confidence intervals

Research concepts: Overview

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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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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Reflections on p-values and confidence intervals

When we run a statistical test, we almost always obtain a p-value. Many statistical tests will also generate a confidence interval. Unfortunately, many scientists report the p-value and ignore the confidence interval. As pointed by Rothman (2016) and the American Statistical Association, relying on p-values forces a false dichotomy between results that are significant and those that are non-significant. This

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What are degrees of freedom in statistics? – Part 2

In a previous post we saw that t distributions with more degrees of freedom approximate the Normal distribution more closely, and degrees of freedom are increased by testing more subjects. How do degrees of freedom influence t values when calculating confidence intervals? The confidence interval about an effect indicates how the effect varies if the study is repeated many times.

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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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