Posts from February 2018 (15)

Ipsos MORI do a nice set of surveys about public misperceptions: ask a sample of people for their estimate of a number and compare it to the actual value.

The newest set includes a question about the proportion of the prison population than are immigrants. Here’s (a redrawing of) their graph, with NZ in all black.

People think more than a quarter of NZ prisoners are immigrants; it’s actually less than 2%. I actually prefer this as a ratio

The ratio would be better on a logarithmic scale, but I don’t feel like doing that today since it doesn’t affect the main point of this pointpost.

A couple of years ago, though, the question was about what proportion of the overall population were immigrants. That time people also overestimated a lot.  We can ask how much of the overestimation for the prison question can be explained by people just thinking there are more immigrants than there really are.

Here’s the ratio of the estimated proportion of immigrants among the prison population and the total population

The bar for New Zealand is to the left; New Zealand recognises that immigrants are less likely to be in prison than people born here. Well, the surveys taken two years apart are consistent with us recognising that, at least.

That’s just a ratio of two estimates. We can also compare to the reality. If we divide this ratio by the true ratio we find out how much more likely people think an individual immigrant is to end up in prison compared to how likely they really are.

It seems strange that NZ is suddenly at the top. What’s going on?

New Zealand has a lot of immigrants, and we only overestimate the actual number by about a half (we said 37%; it was 25% in 2017). But we overestimate the proportion among prisoners by a lot. That is, we get this year’s survey question badly wrong, but without even the excuse of being seriously deluded about how many immigrants there are.

•  Aaron Schiff’s maps of “Small Towns of Aotearoa”
• “The Shame of Being a Statistic”  by Diana Hennessy
• Color palettes: pigment.shapefactory.co (via @fogonwater)
• “From Spreadsheets to Headlines: A Day in the Life of Data Journalist Ryan Struyk“
• Bad data: What happens when a huge number of people share a single grocery store loyalty card? (not much, actually)
• Felix Salmon approves of changes in how Oxfam reports its wealth inequality data.
• “nobody thought to explain to it the long history of Black people being dehumanized by being compared to apes. That context is what made this error so serious and harmful, while misidentifying someone’s toddler as a seal would just be funny”. On AI and its limitations — not a new topic, but an usually good examination.
• In which everywhere is above average: data analysis of tourist expenditures by Peter Ellis.
• Matt Nippert and Keith Ng have a session on data journalism at the New Zealand Festival, if you’re in Wellington on March 9
• Key Concepts for Informed Health Choices: the website and the research paper. ” The concepts in this list are universally relevant. However, our initial focus has been on concepts that are important and possible to teach or communicate to children and poorly educated adults living in low-income countries. “

I’ve seen this map several times on Twitter. It’s originally from a British company, ThirtyFifty. It gives a really nice idea of where the world’s wine regions are, and why, in terms of temperature.

There’s one problem, which is probably more obvious to New Zealanders than northern-hemisphere people. The latitude lines are wrong.

The 50N line and the equator are right, but the 30N line is a little too far north; the 30S line is a little too far north; and the 50S line is way too far north — it’s at about 45S, measured by the southern tip of Tasmania and the middle of NZ’s South Island.

The New York Times and Stuff both have recent stories about fake Twitter followers. There’s an important difference. The Times focuses on a particular company that they claim sells fake followers; Stuff talks about two apps that claim to be able to detect fakes by looking at their Twitter accounts.

The difference matters. If you bought fake followers from a company such as the one the Times describes, then you (or a ‘rogue employee’) knew about it with pretty much 100% accuracy.  If you’re relying on algorithmic identification, you’d need some idea of the accuracy for it to be any use — and an algorithm that performs fairly well on average for celebrity accounts could still be wrong quite often for ordinary accounts. If you know that 80% of accounts with a given set of properties are fake, and someone has 100,000 followers with those properties, it might well be reasonable to conclude they have 80,000 fake followers.  It’s a lot less safe to conclude that a particular follower, Eve Rybody, say, is a fake.

Stuff says

As StatsChat readers will know, there are lots of candidate tests for Alzheimer’s Disease, all of which are much better than just flipping a coin. These tests may be useful in selecting people for clinical trials, and if we ever get effective disease-modifying treatments, for deciding who to treat. At the moment, though, these tests aren’t much use.

BBC News has another one

Scientists in Japan and Australia have developed a blood test that can detect the build-up of toxic proteins linked to Alzheimer’s disease.

The work, published in the journal Nature, is an important step towards a blood test for dementia.

The test was 90% accurate when trialled on healthy people, those with memory loss and Alzheimer’s patients.

If you’re not paying careful attention, 90% accuracy sounds pretty good. But that’s the accuracy in a group of people where about half of them have Alzheimer’s.  In the population, where most people are ok, the false positive rate will still be scarily high.

Also, in contrast to some tests I’ve written about, the main focus of this paper is differentiating Alzheimer’s Disease from other sorts of dementia

The research paper says

The plasma composite biomarker showed 96.7% sensitivity, 81.0% specificity, and 90.2% accuracy in the overall data (n = 51) when predicting individual Aβ status (Aβ⁺ or Aβ⁻) using the common cut-off value (0.376) (Extended Data Fig. 8e–g). The results suggest that the plasma biomarker could be helpful for the differential diagnosis of AD and aid in determining therapeutic strategies, by providing additional information on the brain Aβ deposition status of individuals.

That’s going to be useful when we get treatments, since the treatments for Alzheimer’s probably won’t work on unrelated kinds of dementia, but it doesn’t really fit with the framing in the story

Alzheimer’s disease starts years before patients have any symptoms of memory loss.

The key to treating the dementia will be getting in early before the permanent loss of brain cells.