Posts from November 2015 (39)

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday December 4 2015.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of November 28 – December 4 2015 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

Q: This Herald photo caption says “Men with facial hair showed much higher levels of hostile sexism, the study found.”  Did it?

A: No.

Q: Ok, how about the introduction “Bearded men will just have to take it on the chin – they are, apparently, more sexist.”?

A: Not really.

Q:  <sigh> Was this mice again?

A: No, there aren’t good animal models for these variables, as far as I know.  It was people.

Q: University students?

A: No, that’s so five years ago. It was a survey on Amazon’s Mechanical Turk, of men from the US and India.

Q: A representative survey?

A: Well, they paid people US$0.25 to participate, so I’m guessing maybe not. It’s probably better than using an undergraduate psychology class, though.

Q: And they measured sexism and beards over the internet?

A: The sexism was a standard questionnaire, so I think we should accept it. The participants were shown a set of facial hair images and asked to pick what their facial hair looked like. Probably ok, too.

Q: And men with beards were the most sexist?

A: No, men with moustaches, goatees, or soul patches.

Q: Then men with beards?

A: Then ‘light beards’, then light and heavy stubble, then heavy beards, then clean-shaven, then light medium beards.

Q: So it’s not primarily beards, it’s more other sorts of facial hair?

A: That’s what the study suggests

Q: Just like the photo caption?

A: Up to a point. “Much higher” is pushing it.

Q: How much higher?

A: About a third of a point, on a six point scale. About 1.5% of the variation between men could be predicted by their facial hair.

Q: Is there a bar chart, perhaps cut off at some non-zero value?

A: You’ve been reading the Daily Mail, haven’t you? Yes, there is:

Q: That’s not a very big difference, is it?

A: No

Q: So some men with facial hair are probably ok?

A: Probably.

After the news about Jarrod Gilbert being refused access to crime data, it’s worth looking at what data-use agreements should look like. I’m going to just consider agreements to use data for one’s own research — consulting projects and commissioned reports are different.

On Stuff, the police said

“Police reserves the right to discuss research findings with the academic if it misunderstands or misrepresents police data and information,” Evans said. 

Police could prevent further access to police resources if a researcher breached the agreement, he said. 

“Our priority is always to ensure that an appropriate balance is drawn between the privacy of individuals and academic freedom.

That would actually be reasonable if it only went that far: an organisation has confidential data, you get to see the data, they get to check whether you’ve reported anything that would breach their privacy restrictions. They can say “paragraph 2, on page 7, the street name together with the other information is identifying”, and you can agree or disagree, and potentially get an independent opinion from a mediator, ombudsman, arbitrator, or if it comes to that, a court.

The key here is that a breach of the agreement is objectively decidable and isn’t based on whether they like the conclusions. The problem comes with discretionary use of data. If the police have discretion about what analyses can be published, there’s no way to tell whether and to what extent they are misusing it. Even if they have only discretion about who can use the data, it’s hard to tell if they are using the implied threat of exclusion to persuade people to change results.

Medical statistics has a lot of experience with this sort of problem. That’s why the International Committee of Medical Journal Editors says, in their ‘conflict of interest’ recommendations

Authors should avoid entering in to agreements with study sponsors, both for-profit and non-profit, that interfere with authors’ access to all of the study’s data or that interfere with their ability to analyze and interpret the data and to prepare and publish manuscripts independently when and where they choose.

Under the ICMJE rules, I believe the sort of data-use restrictions we heard about for crime data would have to be disclosed as a conflict of interest.  The conflict wouldn’t necessarily lead to a paper being rejected, but it would be something for editors and reviewers to bear in mind as they looked at which results were presented and how they were interpreted.

The New Zealand Statistical Association is having its annual meeting at the moment in Christchurch. It’s hard for a lot of people to imagine how there could be new research in statistics, so here are some examples from the awards.

Maxine Pfannkuch won the Association’s lifetime achievement award, for her work on statistics education. She studies how people (mostly schoolkids) draw informal statistical conclusions from data and from graphics, and looks for ways to teach them to do it better. A lot of the improvements in the high-school stats curriculum are her fault.

Mark Holmes won the research award. His research is harder to explain in simple terms, but he studies random processes that accumulate over time — like the shape of the trail left by a randomly-moving point.

Blair Robertson won the junior research award. He used to be an applied mathematician, working on optimisation — finding the best value of a complicated function. He now uses similar techniques to come up with improved ways to choose sets of locations in space and time for environmental sampling.

Maarten Kruijver won a `young statistician’ talk award. He works in forensic statistics, looking at ways to estimate the chance that a DNA sample from a crime scene will coincidentally look as if it is from a close relative of someone in the police database.

Anjali Gupta won the other `young statistician’ talk award. She is studying a laser-based technique for measuring chemical composition of things, with forensics being one application.  She was studying the variation in measurements for the same object over time, to understand more about the accuracy of the technique.

Jarrod Gilbert has spent a lot of time hanging out with people in biker gangs.

That’s how he wrote his book, Patched, a history of gangs in New Zealand.  According to the Herald, it’s also the police’s rationale for not letting him have access to crime data. I don’t know whether it would be more charitable to the Police to accept that this is their real reason or not.

Conceivably, you might be concerned about access to these data for people with certain sorts of criminal connections. There might be ways to misuse the data, perhaps for some sort of scam on crime victims. No-one suggests that is  the sort of association with criminals that Dr Gilbert has.

It gets worse. According to Dr Gilbert, also writing in the Herald, the standard data access agreement for the data says police “retain the sole right to veto any findings from release.” Even drug companies don’t get away with those sorts of clauses nowadays.

To the extent these reports are true, we can’t entirely trust any analysis of New Zealand crime data that goes beyond what’s publicly available. There might be a lot of research that hasn’t been affected by censorship and threats to block future work, but we have no way of picking it out.

It’s the time of year when people are asking “What can I buy for my favourite nerd?”. Here are some books s/he might like, a mixture of older and new.

• Thing Explainer by Randall Munroe (of XKCD fame). A coffee-table book of annotated drawings, along the lines of his 2012 Up Goer Five. I reviewed this for the Listener.  It’s really good.
• Eureka: Discovering Your Inner Scientist by Chad Orzel.  I’ve mentioned this book before on StatsChat. It’s a great look at how science works. In the process, it attacks a lot of the myths about scientists.
• Thinking, Fast and Slow by Daniel Kahneman. With Amos Tversky, he pioneered the study of why people are bad at probability and risk assessments. He won a Nobel-like Prize for Economics shortly after the book came out. Unlike many books of its kind, it doesn’t need the subtitle “Why I am Right About Everything”.
• The Immortal Life of Henrietta Lacks, by Rebecca Skloot. The HeLa cell line is a mainstay of laboratory research, but until fairly recently even most scientists didn’t know where it came from. Skloot’s book tells the story of an African-American woman treated for cervical cancer at Johns Hopkins, and how her cells lived on without her or her family’s knowledge.
• How Not To Be Wrong by Jordan Ellenberg. Ellenberg is a highly-respected pure mathematician, but his book is about statistical thinking in everyday life.
• The Canon, by Natalie Angier. A survey of the most important things we’ve learned about the universe. Includes a chapter on probability and statistics featuring the wonderful Deb Nolan.
• The Secret Life of Money, by Daniel Davies and Tess Read. One of the many books trying to explain the world in terms of microeconomics or vice versa. Less everything-you-know-is-wrong and more entertaining writing than Freakonomics.  I’m not sure if reading Davies’s account of a visit to New Zealand will make you more or less likely to want the book.
• The Philadephia Chromosome, by Jessica Wapner. This tells the story of the selective tyrosine kinase inhibitor imitanib (Gleevec),  and its (largely unfulfilled) promise of cancer treatment targeting the cause of disease without toxic side-effects.
• The Disappearing Spoon, by Sam Kean. The eponymous spoon is made of gallium, which melts at about 30C; the book is an entertaining and informative survey of the periodic table.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday November 27 2015.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of November 21 – 27 2015 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

From the Herald

A study by sleep experts at Sealy UK found that those who kip on the right-hand side of the mattress are far more pessimistic than those who doze on the left.

…

 

Neil Robinson, Sealy’s top snooze analyst, said: “The research certainly highlights an interesting trend ” could it be possible that the left side of bed is the ‘right’ side?

1. The Sealy UK press release site doesn’t yet have this story, but the last couple of ‘research’ releases it has are a joint one with the British Chiropractic Association (saying you need expert advice to buy a bed), and a joint one with a psychic and dream interpreter (about common dreams). Both were surveys of 1000 people; no further detail about how they were done. It looks as though this is probably a “study by sleep experts” only in a very liberal sense.
2. In 2011, the same question was studied in another survey (again, not much detail) by a hotel chain, with basically the same results.
3. There are other things that tend, on average, to be different between people sleeping in the same bed and that might affect the answers. For example, the surveys didn’t ask about gender, or at least didn’t mention it  in the results
4. And, of course, even if there is cause and effect it could go the other way about as plausibly.
5. Finally, “Neil Robinson, Sealy’s top snooze analyst” appears in other stories as “Neil Robinson, marketing director of Sealy UK”.