E-values in statistics: apt additions or instruments of generational revolt?
It was never meant to last, you know. Statistical measures have their heydays; permanent relevance is no guarantee. The p-value was – and still is – a tool like no other. Through the years it has been caressed and condemned, worshipped and feared, praised and slandered – all the while standing at the crossroads of almost every hypothesis testing, modeling, and prediction. Operationally, a p-value is convenient: we reject, almost mechanically, our null assumption if this value falls below certain discipline-specific thresholds like 0.01, 0.05, etc. Still, its cumbersome construction, triggering its tricky interpretation and stunning misuses, frequently lands it on the wrong side of both practitioners and stats purists. Bodies such as the American Statistical Association routinely issue caution around its use (https://doi.org/10.1080/00031305.2016.1154108). Experts have been hearing its death rattle for quite a while. The article “E-values: calibration, combination, and applications” by V. Volk and R. Wang could be the final twist of the knife. Here, the authors offer a promising alternative – the e-value – which can coexist with – and, at times, replace – its troubled ancestor.
Predicting the Future (events)
For quality assessments in reliability and industrial engineering, it is often necessary to predict the number of future events (e.g., system or component failures). Examples include the prediction of warranty returns and the prediction of future product failures that could lead to serious property damages and/or human casualties. Business decisions such as a product recall are based on such predictions.
Improving imaging to get the best possible picture of cancer
Have you ever had to retake a photograph on your phone but the sun was shining way too
brightly in the background, causing the subject to appear with a halo? Maybe your arm just
wasn’t within reach and those 10 family members just couldn’t fit inside the frame, leaving
someone just ever so slightly on the outskirts?
Making the Joint Statistical Meetings truly “joint” with a broader audience
I recently got back from the Joint Statistical Meetings in Washington, D.C. where I talked about making audiences concrete and motivating authentic arguments for statistics students (and spread the word about MathStatBites of course). This is a big conference where statisticians from all over the world get together to talk shop, and it was back in person after a few years of going virtual.
Navigating the “Black Hole of Statistics”: Model Selection
A statistical toolbox in some ways is like an endless buffet. There are tons of statistical methods out there, ranging from linear models to statistical tests to neural networks. In addition, with increasing amounts of data, new applications from other fields, and increased computational power, methods are constantly being created or improved upon. Having so many possibilities, of course, has its perks. But researchers inevitably must face this daunting question: what method do you choose and why?
Welcome to MathStatBites!
Hello everyone and welcome to MathStatBites!
We’re a blog site dedicated to sharing current research from the fields of mathematics and statistics.