How can researchers respectfully and constructively flag inadequate statistical evidence when we they see it in papers? This post offers some personal reflections on this complex question.
A primer on evaluating statistical evidence, with a focus on p-curve analysis.
You have used the distBuilder library to collect data, now what? This post walks you through the basics of cleaning and analyzing distribution builder data in R.
A short tutorial on adding "totals" to distBuilder, keeping track of how many balls are allocated in each bucket
A two-part blog post on outlier exclusion procedures, and their impact on false-positive rates.
A two-part blog post on outlier exclusion procedures, and their impact on false-positive rates.
A short case study showing how not to deal with your outliers, featuring a recent paper published in psychology.
Do you wonder how respondents are using distBuilder to construct their final distribution? This blog post shows how you can record the history of allocations that participants have constructed in distBuilder.
You have used the distBuilder library to collect data, now what? This post walks you through the basics of cleaning and analyzing distribution builder data in Python.
You want to test how well people can learn a normal distribution. How can you make sure that the discrete number of values that you will show to participants will look 'normal'? This blog post gives you a simple and foolproof way of generating values for your experiments.