The previous post on this blog sought to expose the statistical underpinnings of several machine learning models you know and love.Read Full Story
A different form of statistical analysis could prove benefitial, but I think the main thing to keep in mind is that data mining algorithms just show you what trends there are in the data, rather than prove anything concretely. If a trend is found in the data, that is the beginning rather than the end of the research.Read Full Story
This week marks one year since the general availability of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP). Here’s how it begins (Excursion 1 Tour 1 (1.1)). Material from the preface is here. I will sporadically give some “one year later” reflections in the comments. I invite readers to ask me any questions pertaining to the Tour.Read Full Story
A key recognition among those who write on the statistical crisis in science is that the pressure to publish attention-getting articles can incentivize researchers to produce eye-catching but inadequately scrutinized claims. We may see much the same sensationalism in broadcasting metastatistical research, especially if it takes the form of scapegoating or banning statistical significance.
… and how do statistical institutions present what they do?
In times of fake news and austerity measures, statistical offices are feeling more and more the urge to orientate the public about themselves and the usefulness and necessity of trustworthy statistics.
But how to proceed?
Public relations specialists know countless ways to get messages to the target groups.
There is huge number of machine learning methods, statistical tools and data mining techniques available for a given data related task, from self organizing maps to Q-learning, from streaming graph algorithms to gradient boosted trees. Many of these methods, while powerful in specific domains and problem setups, are arcane and utilized or even understood by few.Read Full Story
I already mentioned the Hastie & Tibshirani course on statistical learning as one of my personal highlights in data science last year.Read Full Story
The end for the traditional statistical yearbooks – be they printed or as ebooks – is approaching gradually. The German yearbook has recently been hit. The last edition had its farewell at a press conference on 30 October 2019: “Digitisation is shaping the statistics of the 21st century.Read Full Story
My colleagues published the Slovene multi-player statistical quiz app on Tuesday 22nd. I love it!
We’ve all heard “statistics is boring”, but once you add lovely design, humorous content and a strategic game to it, it can be fun. In two days after the announcement there are more than 1200 players who already played abt. 24.000 games.
Logistic regression is one of the statistical techniques in machine learning used to form prediction models. It is one of the most popular classification algorithms mostly used for binary classification problems (problems with two class values; however, some variants may deal with multiple classes as well). It’s used for various research and industrial problems.Read Full Story
Part 3 of Dare to Compare shows how one-population statistical tests are conducted. Part 4 extends these concepts to two-population tests. To review, this flowchart summarizes the the process of statistical testing.Read Full Story
Parts 1 and 2 of Dare to Compare summarized fundamental topics about simple statistical comparisons. Part 3 shows how those concepts play a role in conducting statistical tests. The importance of these concept are highlighted in the following table.Read Full Story
The American Statistical Association has identified 146 college majors that require statistics to complete a degree. You probably wouldn’t be surprised that statistics is required for degrees in mathematics, engineering, physics, astronomy, chemistry, meteorology, and even biology and geology.Read Full Story
Now, everything is connected, but this is not primarily about persistent research misconceptions such as statistical significance.
Instead it is about (inherently) interpretable ML versus (misleading with some nonzero frequency) explanatory ML that I previously blogged on just over a year ago.
During a recent talk (I think it was this one on statistical visualization), I spent a few minutes discussing a political science experiment involving social stimuli and attitudes toward redistribution.Read Full Story
A jury thrilled with Bayesian statistical learning
Long time no see, Statisfaction!
I’m glad to write about my habilitation entitled Bayesian statistical learning and applications I defended yesterday at Inria Grenoble. This Habilitation à Diriger des Recherches (HDR) is the highest degree issued through a university examination in France.