Blog Archives

[Updated] Statistical Power and Significance Testing Visualization

August 24, 2015
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My Statistical Power and Significance Testing Visualization now lets you vary effect size, sample size, power and significance level. There's also a new feature to rescale the plot and by clicking-and-dragging you can pan the visualization.

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[Update] P-curve visualization updated with log x-axis

August 13, 2015
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[Update] P-curve visualization updated with log x-axis

My p-curve tool now lets you show the x-axis on a log₁₀ scale, which makes it a lot easier to look at really small p-values. Thanks to Ged Ridgway for suggestion this!

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Using R and lme/lmer to fit different two- and three-level longitudinal models

April 21, 2015
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Using R and lme/lmer to fit different two- and three-level longitudinal models

I often get asked how to fit different multilevel models (or individual growth models, hierarchical linear models or linear mixed-models, etc.) in R. In this guide I have compiled some of the more common and/or useful models (at least common in clinical psychology), and how to fit them using nlme::lme() and lme4::lmer(). I will cover the common two-level random...

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New visualization of the distribution of p-values using d3.js

March 21, 2015
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New visualization of the distribution of p-values using d3.js

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I am trying to show how p-values are distributed. Check it out here: rpsychologist.com/d3/pdist/

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Interpreting Confidence Intervals – new d3.js visualization

November 28, 2014
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Interpreting Confidence Intervals – new d3.js visualization

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I have tried to explain confidence intervals for means. This visualization shows a simulation of repeated sampling from a normal dist...

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New d3.js visualization: Interpreting Correlations

August 1, 2014
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New d3.js visualization: Interpreting Correlations

Here is a new visualization done in d3js. In this visualization I show a scatter plot of two variables with a given correlation. The variables are samples from the standard normal distribution, which are then transformed to have a given correlation by...

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Some exploratory evidence that wait-list conditions may act as a nocebo in psychotherapy trials

April 14, 2014
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Some exploratory evidence that wait-list conditions may act as a nocebo in psychotherapy trials

The double-blinded placebo-controlled randomized trial have long been held as the gold standard in pharmacological research. Unfortunately, this design is impossible to mimic in clinical psychology. Even if we — at best — could try to keep the participants blinded to their treatment allocation, it would be rather hard to blind therapists to what therapy they are giving. The...

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New d3.js visualization: Understanding Significance Testing and Statistical Power

February 2, 2014
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New d3.js visualization: Understanding Significance Testing and Statistical Power

Here is a new visualization created in the same manner as my Cohen’s d vizualisation. This new visualization is an interactive display of classical null hypothesis significance testing and statistical power. The visualization should work on mobile phones and tablets, but it requires a modern browser that supports SVG. Check...

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Expected overestimation of Cohen’s d under publication bias

January 27, 2014
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Expected overestimation of Cohen’s d under publication bias

Earlier this week I read this article about “Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results” by Mercal et al (2014). The authors simulated different meta-analytic scenarios and came to the conclusion that publishing everything is more effective for the scientific collective. This got me thinking about...

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Expected overestimation of Cohen’s d under publication bias

January 27, 2014
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Expected overestimation of Cohen’s d under publication bias

In this post I will use the theoretical and empirical sampling distribution of Cohen's d to show the expected overestimation due to selective publishing. I will look at the overestimation for various sample sizes when the population effect is 0, 0.2, 0.5 and 0.8. The conclusion is that you should be weary of effect sizes from small samples, and...

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