Yesterday’s episode of The Nation had an interesting piece on the work that Statistics New Zealand does, its history and relevance to all New Zealanders:
Statistics New Zealand is also a finalist in the upcoming public sector excellence awards in three categories.
In Lake Wobegon all the women are strong, all the men are good looking, and all the children are above average. We laugh because it is impossible for everybody to be above average. It is quite possible, however, for 80% of the population to be above average. Take ten people two of whom die at birth and eight die on their hundredth birthdays. In our sample the average life expectancy is 80, and 80% live longer than average. Even if our two unfortunates live until they are 99, 80% still live longer than average. So for any fraction, p%, other than 0% or 100% it is entirely possible for “p% of the population to be taller /shorter/ fatter / thinner / longer lived / or… than average.”
A blog post by a veteran editor, on one of the recurring stories about ‘lucky’ lottery-ticket outlets, explains elegantly and simply why it’s a real problem:
In some cosmic sense, sharing popular delusions about the lottery isn’t any worse than publishing the horoscopes. But if you’d like to be taken seriously when you proclaim “study says” or “poll reveals,” you need to run a disclaimer with your “want to hit the lottery” tales:
Along similar lines to our Stats Crimes, Flowing Data has posted a list of 5 common statistical fallacies and examples:
If you have a research finding that is statistically borderline but makes sense biologically, it often seems that the biological explanation should make it more convincing. Unfortunately, people are really good at explanations and almost any set of flimsy starting materials can be woven together into a superficially firm explanatory facade. Keith Baggerley, who analyses gene expression data, says that the cancer researchers he works with can always find explanations for a list of genes that are overactive in a particular experiment — and he’s verified this by giving them completely random gene lists.
The Herald has a nice example in an article on functional foods:
You might be surprised to learn that you can often visually identify the function of natural foods. …Walnuts are nicknamed “brain food” and they look just like tiny brains complete with left and right hemispheres.
Sliced tomatoes resemble the structure of the heart with multiple chambers and, you guessed it, are credited with reducing the risk of heart disease. The list goes on – avocados for the uterus, celery for bones. It seems functional foods have always existed if you look closely.
It helps if you are allowed to stretch both the conditions for similarity and for functionality — celery isn’t distinctively good for bones, and the similarity of avocado to the uterus depends enormously on which variety you choose (or perhaps the Reed cultivar, in season now, and the seedless cocktail avocados are supposed to have other functions). It also helps if you can ignore badly-fitting examples: a Canadian paper recently had a similarly credulous story about mushrooms as a functional food, but edible mushrooms are of many different shapes, and the common cultivated mushroom shares its shape with things that you really don’t want to eat.
I won’t even comment on what organ the carrot’s shape shows it supposed to benefit.
The recent Sky City stories illustrate an interesting division in media reports of gambling
The first international competition to recognize outstanding work in the growing field of data journalism, the Data Journalism Awards, has just announced the 58 nominees for the 2012 awards.
The competition is being run by the Global Editors Network, supported by Google and in collaboration with the European Journalism Centre.
The University of Auckland’s Jonathan Albright is one of the nominees for his data-driven investigation Did Twitter censor Occupy Wall Street. Congratulations Jonathan!
Winners will be announced on May 31 during the News World Summit in Paris.
According to the Herald, “Baboons learn to tell real words from gibberish”, which would put them ahead of some politicians and academics. The story describes
… new research that shows baboons are able to pick up the first step in reading – identifying recurring patterns and determining which four-letter combinations are words and which are gobbledygook.
Here’s the press release, and the scientific paper. After being trained with a bunch of real and made-up words with four letters (no, not the ones you’re thinking of), the baboons could classify new four-letter combinations with about 75% accuracy, compared to 50% for pure chance.
The scientists were a bit more restrained, saying that the baboons were probably looking at which two-letter pairs appeared more frequently in the real words. In fact, as Mark Liberman describes at Language Log, it was probably even simpler than that. If the baboons just recognised the shapes of individual letters, that would be enough for them to get 75% accuracy. If they were really doing anything more sophisticated they should be doing much, much better than 75% accuracy.
One thing I don’t understand, though, is why French psychologists would try to teach the baboons to recognise English words.
How do we know whether a particular drug, therapy or operation really works, and how well? How reliable is the evidence? Are clinical trials truly unbiased?
Recommended for their plain-language approach to answering these questions are Testing Treatments: Better research for better healthcare by three doctors and a health advocate, and the University of Sydney’s Smart Choices: Making sense of health advice.
And even better, both are free downloads.
