Posts filed under Evidence (73)

January 21, 2015

Meet Statistics summer scholar Alexander van der Voorn

Alex van der VoornEvery year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Alexander, right, is undertaking a statistics education research project with Dr Marie Fitch and Dr Stephanie Budgett. Alexander explains:

“Essentially, what this project involves is looking at how bootstrapping and re-randomisation being added into the university’s introductory statistics course have affected students’ understanding of statistical inference, such as interpreting P-values and confidence intervals, and knowing what can and can’t be justifiably claimed based on those statistical results.

“This mainly consists of classifying test and exam questions into several key categories from before and after bootstrapping and re-randomisation were added to the course, and looking at the change (if any) in the number of students who correctly answer these questions over time, and even if any common misconceptions become more or less prominent in students’ answers as well.

“This sort of project is useful as traditionally, introductory statistics education has had a large focus on the normal distribution and using it to develop ideas and understanding of statistical inference from it. This results in a theoretical and mathematical approach, which means students will often be restricted by the complexity of it and will therefore struggle to be able to use it to make clear inference about the data.

“Bootstrapping and re-randomisation are two techniques that can be used in statistical analysis and were added into the introductory statistics course at the university in 2012. They have been around for some time, but have only become prominent and practically useful recently as they require many repetitions of simulations, which obviously is better-suited to a computer rather than a person. Research on this emphasises how using these techniques allow key statistical ideas to be taught and understood without a lot of fuss, such as complicated assumptions and dealing with probability distributions.

“In 2015, I’ll be completing my third year of a BSc in Statistics and Operations Research, and I’ll be looking at doing postgraduate study after that. I’m not sure why statistics appeals to me, I just found it very interesting and enjoyable at university and wanted to do more of it. I always liked maths at school, so it probably stemmed from that.

“I don’t have any plans to go away anywhere so this summer I’ll just relax, enjoy some time off in the sun and spend time around home. I might also focus on some drumming practice, as well as playing with my two dogs.”

January 16, 2015

Women are from Facebook?

A headline on Stuff: “Facebook and Twitter can actually decrease stress — if you’re a woman”

The story is based on analysis of a survey by Pew Research (summary, full report). The researchers said they were surprised by the finding, so you’d want the evidence in favour of it to be stronger than usual. Also, the claim is basically for a difference between men and women, so you’d want to see summaries of the evidence for a difference between men and women.

Here’s what we get, from the appendix to the full report. The left-hand column is for women, the right-hand column for men. The numbers compare mean stress score in people with different amounts of social media use.

pew

The first thing you notice is all the little dashes.  That means the estimated difference was less than twice the estimated standard error, so they decided to pretend it was zero.

All the social media measurements have little dashes for men: there wasn’t strong evidence the correlation was non-zero. That’s not we want, though. If we want to conclude that women are different from men we want to know whether the difference between the estimates for men and women is large compared its uncertainty.  As far as we can tell from these results, the correlations could easily be in the same direction in men and women, and could even be just as  strong in men as in women.

This isn’t just a philosophical issue: if you look for differences between two groups by looking separately for a correlation each group rather than actually looking for differences, you’re more likely to find differences when none really exist. Unfortunately, it’s a common error — Ben Goldacre writes about it here.

There’s something much less subtle wrong with the headline, though. Look at the section of the table for Facebook. Do you see the negative numbers there, indicating lower stress for women who use Facebook more? Me either.

 

[Update: in the comments there is a reply from the Pew Research authors, which I got in email.]

August 2, 2014

When in doubt, randomise

The Cochrane Collaboration, the massive global conspiracy to summarise and make available the results of clinical trials, has developed ‘Plain Language Summaries‘ to make the results easier to understand (they hope).

There’s nothing terribly noticeable about a plain-language initiative; they happen all the time.  What is unusual is that the Cochrane Collaboration tested the plain-language summaries in a randomised comparison to the old format. The abstract of their research paper (not, alas, itself a plain-language summary) says

With the new PLS, more participants understood the benefits and harms and quality of evidence (53% vs. 18%, P < 0.001); more answered each of the five questions correctly (P ≤ 0.001 for four questions); and they answered more questions correctly, median 3 (interquartile range [IQR]: 1–4) vs. 1 (IQR: 0–1), P < 0.001). Better understanding was independent of education level. More participants found information in the new PLS reliable, easy to find, easy to understand, and presented in a way that helped make decisions. Overall, participants preferred the new PLS.

That is, it worked. More importantly, they know it worked.

July 30, 2014

If you can explain anything, it proves nothing

An excellent piece from sports site Grantland (via Brendan Nyhan), on finding explanations for random noise and regression to the mean.

As a demonstration, they took ten baseball batters and ten pitchers who had apparently improved over the season so far, and searched the internet for news that would allow them to find an explanation.  They got pretty good explanations for all twenty.  Looking at past seasons, this sort of short-term improvement almost always turns out be random noise, despite the convincing stories.

Having a good explanation for a trend feels like convincing evidence the trend is real. It feels that way to statisticians as well, but it isn’t true.

It’s traditional at this point to come up with evolutionary psychology explanations for why people are so good at over-interpreting trends, but I hope the circularity of that approach is obvious.

July 29, 2014

A treatment for unsubstantiated claims

A couple of months ago, I wrote about a One News story on ‘drinkable sunscreen’.

In New Zealand, it’s very easy to make complaints about ads that violate advertising standards, for example by making unsubstantiated therapeutic claims. Mark Hanna submitted a complaint about the NZ website of the company  selling the stuff.

The decision has been released: the complaint was upheld. Mark gives more description on his blog.

In many countries there is no feasible way for individuals to have this sort of impact. In the USA, for example, it’s almost impossible to do anything about misleading or unsubstantiated health claims, to the extent that summoning a celebrity to be humiliated publicly by a Senate panel may be the best option.

It can at least produce great television: John Oliver’s summary of the Dr Oz event is viciously hilarious

July 14, 2014

Multiple testing, evidence, and football

There’s a Twitter account, @FifNdhs, that has five tweets, posted well before today’s game

  • Prove FIFA is corrupt
  • Tomorrow’s scoreline will be Germany win 1-0
  • Germany will win at ET
  • Gotze will score
  • There will be a goal in the second half of ET

What’s the chance of getting these four predictions right, if the game isn’t rigged?

Pretty good, actually. None of these events is improbable on its own, and  Twitter lets you delete tweets and delete accounts. If you set up several accounts, posted a few dozen tweets on each, describing plausible events, and then deleted the unsuccessful ones, you could easily come up with an implausible-sounding remainder.

Twitter can prove you made a prediction, but it can’t prove you didn’t also make a different one, so it’s only good evidence of a prediction if either the predictions were widely retweeted before they happened, or the event described in a single tweet is massively improbable.

If @FifNdhs had predicted a 7-1 victory for Germany over Brazil in the semifinal, that would have been worth paying attention to. Gotze scoring, not so much.

May 22, 2014

Briefly

Health and evidence edition

  • Evidently Cochrane, a blog with non-technical explanations of Cochrane Collaboration review results
  • Design process for a graphic illustrating the impact of motorbike helmet laws.  In contrast to bicycle helmet laws, laws for motorbikes do have a visible effect on death statistics
  • Stuff has quite a good story on alcohol in New Zealand.
  • The British Association of Dermatologists responds to ‘drinkable sunscreen’.
  • 3News piece on Auckland research into extracts of the lingzhi mushroom. Nice to see local science, and the story was reasonably balanced, with Shaun Holt pointing out that this is not even approaching being anywhere near evidence that drinking the stuff would do more good than harm.
May 8, 2014

Think I’ll go eat worms

This table is from a University of California alumni magazine

Screen-Shot-2014-05-06-at-9.06.38-PM

 

Jeff Leek argues at Simply Statistics that the big problem with Big Data is they, too, forgot statistics.

May 2, 2014

Mammography ping-pong

Hilda Bastian at Scientific American

It’s like a lot of evidence ping-pong matches. There are teams with strongly held opinions at the table, smashing away at opposing arguments based on different interpretations of the same data.

Meanwhile, women are being advised to go to their doctors if they have questions. And their doctors may be just as swayed by extremist views and no more on top of the science than anyone else.

She explains where the different views  and numbers come from, and why the headlines keep changing.

April 25, 2014

Sham vs controlled studies: Thomas Lumley’s latest Listener column

How can a sham medical procedure provide huge benefits? And why do we still do them in a world of randomised, blinded trials? Thomas Lumley explores the issue in his latest New Zealand Listener column. Click here.