An email discussion about the appropriate percentage effort on research grants has produced a lot of interesting discussions. One person raised an interesting question. The typical data analysis, he claimed, might involve a few hours reviewing the input data set, a few hours conducting the analysis and a few hours preparing a statistical summary, but even after a generous estimate of the work at each of the time points, he could only come up with 22 hours of effort, which corresponds roughly with a 1% FTE. I wrote back describing some of the things that might occur before the data analysis that might add time to this effort. Continue reading
I heard a story a long time ago, and I don’t remember who told it to me and I’m probably getting all the details wrong, but I wanted to try to recreate the story from memory because it illustrates one of the perils of blind reliance on statistical models to identify “important” variables. Continue reading
I’ve gotten an inquiry about teaching a couple of webinars. Nothing’s official yet, but let me outline the these webinars here on my blog. If the offer becomes official, I will update on this blog post or on a new post. Continue reading
Doug Zahn has done a tremendous amount of work on what I like to call the human factors in statistical consulting. He summarizes some key ideas in this article. His humorous anecdote about his prized Mustang car illustrates the tendency of all of us to be poor listeners. Pay special atention to Table 1 where he outlines the five steps you should always follow in any consulting interaction. Continue reading
I was talking about pricing models for consulting in an email exchange and I thought I’d extract some of those comments for this blog. When you are an independent consultant, you need to decide whether you will charge by the hour or charge a flat fee for the entire project. Continue reading
This is a short overview of five major social media sites: LinkedIn, Twitter, Facebook, Instagram, and Snapchat and how you might use them to promote your career. The article ends with a few good overall suggestions. Continue reading
This is one of the best articles I have ever read in the popular press about the complexities of the research process.
This article by Susan Dominus covers some high profile research by Amy Cuddy. She and two co-authors found that your body language not only influences how others view you, but it influences how you view yourself. Striking a “power pose” meaning something like a “legs astride or feet up on a desk” can improve your sense of power and control and these subjective feelings are matched by physiological changes, Your testosterone goes up and your cortisol goes down. Both of these, apparently, are good things.
The research team publishes these findings in Psychological Science, a prominent journal in this field. The article receives a lot of press coverage. Dr. Cuddy becomes the public face of this research, most notably by garnering an invitation to give a TED talk and does a bang-up job. Her talk becomes the second most viewed TED talk of all time.
But there’s a problem. The results of the Psychological Science publication do not get replicated. One of the other two authors expresses doubt about the original research findings. Another research team reviews the data analysis and labels the work “p-hacking”.
It turns out that there is a movement in the research world to critically examine existing research findings and to see if the data truly supports the conclusions that have been made. Are the people leading this movement noble warriors for truth or are they shameless bullies who tear down peer-reviewed research in non-peer-reviewed blogs.
I vote for “noble warriors” but read the article and decide for yourself what you think. It’s a complicated area and every perspective has more than one side to it.
One of the noble warriors/shameless bullies is Andrew Gelman, a popular statistician and social scientist. He comments extensively about the New York Times article on his blog, which is also worth reading as well as many comments that others have made on his blog post. It’s also worth digging up some of his earlier commentary about Dr. Cuddy. Continue reading
Dear Professor Mean, I saw a paper where the authors said that they wanted a diagnostic test with a large negative likelihood ratio, because it was important to rule out a condition. False negatives mean leaving a high risk condition untreated. But don’t they mean that they want a diagnostic test with a small likelihood ratio?
Okay, I agree with you, but it’s an understandable mistake. Let’s quickly review the idea of likelihood ratios. A positive likelihood ratio is defined at Sn / (1-Sp) where Sn is the sensitivity of the diagnostic test and Sp is the specificity. For a diagnostic test with a very high specificity, you get a very large ratio, because you are putting a really small value in the denominator. For Sp=0.99, for example, you would end up getting a positive likelihood ratio of 50 or more (assuming that Sn is at least 0.5).
The positive likelihood ratio is a measure of how much the odds of disease are increased if the diagnostic test is positive.
A negative likelihood ratio is defined as as (1-Sn) / Sp. For a diagnostic test with a very large sensitivity, the negative likelihood ratio is very close to zero. For Sn=0.99, the likelihood ratio is going to be 0.02 or smaller, assuming that Sp is at least 0.5.
The negative likelihood ratio is a measure of how much the odds of disease are decreased if the diagnostic test is negative.
The two likelihood ratios should remind you of the acronyms SpIn and SnOut. SpIn means that if specificity is large, then a positive diagnostic test is good at ruling in the disease. This isn’t always the case, sadly, and for many diagnostic tests, the next step after a positive test is not to treat the disease, but to double check things using a more expensive or more invasive test.
SnNout means that if the sensitivity is large, then a negative diagnostic test is good at ruling out the disease. You can safely send the patient home in some settings, or start looking for other diseases in different settings.
That sounds great, but sometimes you are very concerned about false negatives, and you don’t want to send someone home if they actually have the disease. If you are worried about a cervical fracture, ruling out the fracture and sending someone home might lead to paralysis or death if you have a false negative. So you want to be very sure of yourself in this setting.
Now with regard to the comment above, I think it is just a case of careless language. When the authors say “large negative likelihood ratio”, they should have said “extreme negative likelihood ratio” meaning a likelihood ratio much much smaller than one. I’ve done it myself when I talk about a correlation of -0.8 as being a “big” correlation because it is very far away from zero.
We tend to shy away from words like “small” when we talk about a negative likelihood ratio being much less than 1, because “small” in some people’s minds means “inconsequential” when the opposite is true. When I am careful in my language, I try to use the word “extreme” to mean very far away from the null value (1 for a likelihood ratio or 0 for a correlation) rather than “large” or “small”.
This is a nice summary about the prosecution of a statistician, Andreas Georgiu, who was only doing his job. Continue reading
Someone on the Statistical Consulting Section message board asked a question about how to handle a situation where a colleague was repeatedly asking for advice. How do you make a transition from offering free advice to getting paid as a consultant? There were lots of good answers, and here’s the suggestion that I offered. Continue reading