Median Watch

Eyes on statistics

A change to judging career disruption

Re-posted from this 2016 AusHSI blog because this is still an issue. Let’s start with the obvious. Winning funding for health and medical research is soul-crushingly hard. Success rates for major schemes are under 20%, so failure is the norm. Your application will be judged by a panel of 6 to 12 senior researchers. A key marker of success is your track record, which may simply mean the number and quality of your papers, and your previous research funding (a very circular measure).

Dear p-values, it's not me, it's not you, it's everyone else

Yet another p-value run-in. For a recent observational study I tried to limit the use of p-values in the paper. My colleagues wanted more p-values and I had to politely push back. During one team meeting I even offered to put the p-values in if someone could accurately tell me what they meant … silence. Predicting that the reviewers would also want to see more p-values, I added this sentence to the paper’s methods: “We have tried to limit the use of p-values, as they are often misunderstood or misinterpreted, and elected to discuss clinically meaningful differences.

Not waving but drowning in data

Our paper examining trends in acronyms in abstracts was recently published in eLife. We examined over 26 million abstracts from the PubMed database, which is easily the largest data set I’ve ever used. In this post I talk about some of the challenges and benefits of dealing with such a massive data set. Data greed One of the most common mistakes I see researchers — new and experienced — make is to collect too much data.

Goodbye to all that

Today I end my two year stint as president of the Statistical Society of Australia. As I press “submit” on my presidency, here’s a hodge-podge of reflections. I was delighted to be president, and I will miss being able to say, “I’m the president of the Stats Society”. Statistics has given me an incredible career and I feel I owe something back. It might sound strange to feel a debt to a thing, but statistics is a big thing.

When should I quit research? An evidence-based approach

Re-posted from the AusHSI blog (8 May 2015). After yet another failed fellowship application I considered if I should leave research. I now have seven fellowship failures and no successes, and that seems like a lot. Success rates for grants are nose-diving and even the former head of the NHMRC says that researchers should be considering other careers (pay-walled). I’ve spent a lot of time running research projects for no money, but working for no money is a luxury I can’t afford.