Hands-on Tutorials

Planning our wedding party with Bayesian- vs. Frequentist statistics.

Photo by Photos by Lanty on Unsplash

It was summer 2019, I looked at a cloudy sky in Croatia, when I deeply changed my mind about statistics. After reading “der Fluch des p-Werts” (engl.: the curse of the p-value) by Regina Nuzzo, it dawned on me — statistical evidence was not the thing I once thought it would be: even reasonably powered “highly significant” results of sensibly planned randomized experiments could possibly turn into false alarms. Because even the brightest scientist cannot switch off the natural uncertainty that comes with statistical estimation. But there was another thing that intrigued me even more: Even confidence intervals — a…

Hands-on Tutorials

Missing data are everywhere — learn how to summarise, visualize & impute them while keeping an eye on statistical uncertainty.

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When values should have been reported but were not available, we end up with missing values. In real-life data, missing values occur almost automatically — like a shadow nobody really can get rid of. Think of nonresponse in surveys, technical issues during data collection or joining data from different sources — annoyingly enough, data for which we have only complete cases are rather scarce. But why should you care about it? And what can go wrong with simply ignoring missing data?

This article will show you why missing data require special treatment and why it is worth it.

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A guide to the most famous and yet most confusing concept in traditional statistics: null hypothesis significance testing (NHST).

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Have you ever heard somebody say that a study revealed „significant results“? What does that even mean? Let me introduce you to a practice in the scientific industry that is deeply debated and still used to answer research questions. Simply put, it roughly goes like this: If you run a model and your computer gives you a p less than .05, your results have reached the holy grail of being statistically significant and therefore are more likely to be published. On the other hand, if p is over .05, your observations do not seem to deviate from what was already…

An easy approach to visualize your within-subject data meaningfully.

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As soon as the same individuals — or things — get measured more than once, we deal with repeated measures data, a scenario that is very common in data science and experimental research. In these cases, you have the chance to estimate the degree to which individual differences account for variability in the data — as well as the extent to which the observed data are probably due to an experimental manipulation (which is what most people are interested in!). However, the exploration and presentation of repeated measures data are not as straightforward compared to between-subject designs, thus your within-subject…

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There is a huge variety of possible graphs to choose from for data visualization (R Graph Gallery). It paves the way to visualize all imaginable data in a breath-taking way — but also bears the risk of forgetting the bigger picture too quickly. This is what probably happened to me. If there were only two types of data analysts in the world, I would definitely be rather someone who loses oneself in the aesthetic and fancy aspect of the graphs than just sticking to my all-time-favourite.

Even though it has become so easy to create amazing graphs with popular programming…

Photo by Robert Thiemann on Unsplash

While any programmer in the world encounter bugs in their code from time to time, there is no common sense how to handle them from a psychological point of view. If you are a very beginner, this may be a challenge for you. Apart from my personal coding story, but also some technical and psychological advice for you.

My personal coding story

I can vividly remember the first day at university when we run our first R code and an error popped up. It may looked like this:

Hannah Wnendt

psychologist and consultant with a passion for data science.

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