Statistical inferences using p-values

And another quick post for today. Here is a nice infographic I just found on the Nature News page. Nice demonstration how p-values can fail us in making hypothesis inferences. Just another article bashing p-values you could say. Or “Just switch already to Bayesian stats or report real effect strengths instead of p-values”. Although the matter is clear for many ecologists out there, the majority still happily uses p-values inferring that they proved their working hypothesis wrong or true. At my former and also at my current university p-values are still being taught and used in all courses related to data analysis. Students are being asked and expected to always (!) report the p-value and trained to look specifically for something they claim is statistical significance of an effect. And then people are wondering why the hell everyone still uses century old techniques. Often while not even knowing what it exactly means. I certainly believe (and I say that while being still educated 🙂 ) that especially in the education of future ecologists and conservationists statistics courses should become mandatory for all (under)graduates. In times of big data analysis basic statistical knowledge has to be a must for everyone.

Courtesy of Nature, Nuzzo 2014

The related Nature News article can be found here. More nice infos and facts about my research in Africa and fieldwork trip will appear around May.

EDIT: And as a funny addition check out this awesome R-function which gives you an appropriate significance description for every p-value 😀



About Martin Jung

PhD researcher at the University of Sussex. Interested in nature conservation, ecology and biodiversity as well as statistics, GIS and 'big data'
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