## Articles with the Ggplot2 tag

A common way of illustrating the idea behind statistical power in null hypothesis significance testing, is by plotting the sampling distributions of the null hypothesis and the alternative hypothesis. Typically, these illustrations highlight the regions that correspond to making a type II error, type I error and correctly rejecting the null hypothesis (i.e. the test’s power). In this post I will show how to create such “power plots” using both ggplot and R’s base graphics.

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In this post I show some different examples of how to work with map projections and how to plot the maps using ggplot. Many maps that are using the default projection are shown in the longlat-format, which is far from optimal. Here I show how to use either the Robinson or Winkel Tripel projection.

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In this post I show some R-examples on how to perform power analyses for mixed-design ANOVAs. The first example is analytical—and adapted from formulas used in G*Power (Faul et al., 2007), and the second example is a Monte Carlo simulation.

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Can you tell when error bars based on 95 % CIs or standard errors correspond to a significant p-value? Don’t fret if you think it’s hard, a study from 2005 showed that researchers in psychogoly, behavior neuroscience and medicine had a hard time judging when error bars from two independent groups signified a significant difference

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Why are physicists talking about 5-sigma, and what’s it got to do with statistics? In this short post I’ll explain what 5-sigma is and why it’s not a measure of how certain scientist are that they’ve found the Higgs boson

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When talking about confidence intervals, Jacob Cohen famously said: “I suspect that the main reason they are not reported is that they are so embarrassingly large!” (Cohen, 1994). In this post I’ll take a look at the relationship between the 95 % CI for Cohen’s d and it’s corresponding sample size.

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Which country had the most PubMed citations in 2011? To find out I used R statistical software to analyze the affiliation of 986 427 articles

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Yesterday I wrote an article that looked at the top 20 Cognitive Behavior Therapy journals with the most publications; today I will explain how I did it with R.

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In this short article I present some data of the top 20 Cognitive Behavior Therapy (CBT) journals with the most PubMed publications, and compare that to data from 2010 and 2011.

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In this short post I take a look at how to use R and ggplot2 to visualize effect sizes (Cohen’s d) and how to shade the overlapping area of two distributions.

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