Posts filed under Experiments (28)

August 28, 2015

Trying again

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This graph is from the Open Science Framework attempt to replicate 100 interesting results in experimental psychology, led by Brian Nozek and published in Science today.

About a third of the experiments got statistically significant results in the same direction as the originals.  Averaging all the experiments together,  the effect size was only half that seen originally, but the graph suggests another way to look at it.  It seems that about half the replications got basically the same result as the original, up to random variation, and about half the replications found nothing.

Ed Yong has a very good article about the project in The Atlantic. He says it’s worse than psychologists expected (but at least now they know).  It’s actually better than I would have expected — I would have guessed that the replicated effects would average quite a bit smaller than the originals.

The same thing is going to be true for a lot of small-scale experiments in other fields.

August 17, 2015

How would you even study that?

From XKCD

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“How would you even study that?” is an excellent question to ask when you see a surprising statistic in the media. Often the answer is “they didn’t,” but sometimes you get to find out about some really clever research technique.

January 30, 2015

Meet Statistics summer scholar Ying Zhang

Ying Zhang Photo

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Ying, right, is working on a project called Service overview, client profile and outcome evaluation for Lifeline Aotearoa Face-to-Face Counselling Services  with the Department of Statistics’ Associate Professor David Scott and Christine Dong, research and clinical engagement manager, Lifeline and also an Honorary Research Fellow in the Department of Psychological Medicine at the University of Auckland. Ying explains:

“Lifeline New Zealand is a leading provider of dedicated community helpline services, face-to-face counselling and suicide prevention education. The project aims to investigate the client profile, the clinical effectiveness of the service and client experiences of, and satisfaction with, the face-to-face counselling service.

“In this project, my work includes three aspects: Data entry of client profiles and counselling outcomes; qualitative analysis of open-ended questions and descriptive analysis; and modelling for the quantitative variables using SAS.

“Very few research studies have been done in New Zealand to explore client profiles or find out clients’ experiences of, and satisfaction with, community face-to-face counselling services. Therefore, the study will add evidence in terms of both clinical effectiveness and client satisfaction. This study will also provide a systematic summary of the demographics and clinical characteristics of people accessing such services. It will help provide direction for strategies to improve the quality and efficiency of the service.

“I have just graduated from the University of Auckland with a Postgraduate Diploma in Statistics.  I got my bachelor and master degrees majoring in information management and information systems at Zhejiang University in China.

“My first contact with statistics was around 10 years ago when I was at university in China. It was an interesting but complex subject for me. After that, I did some internship work relating to data analysis. It helped me accumulate more experience about using data analysis to help inform business decisions.

“This summer, apart from participating in the project, I will spend some time expanding my knowledge of SAS – it’s a very useful tool and I want to know it better. I’m also hoping to find a full-time job in data analysis.”

 

 

 

 

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.”

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 24, 2014

Weak evidence but a good story

An example from Stuff, this time

Sah and her colleagues found that this internal clock also affects our ability to behave ethically at different times of day. To make a long research paper short, when we’re tired we tend to fudge things and cut corners.

Sah measured this by finding out the chronotypes of 140 people via a standard self-assessment questionnaire, and then asking them to complete a task in which they rolled dice to win raffle tickets – higher rolls, more tickets.

Participants were randomly assigned to either early morning or late evening sessions. Crucially, the participants self-reported their dice rolls.

You’d expect the dice rolls to average out to around 3.5. So the extent to which a group’s average exceeds this number is a measure of their collective result-fudging.

“Morning people tended to report higher die-roll numbers in the evening than the morning, but evening people tended to report higher numbers in the morning than the evening,” Sah and her co-authors wrote.

The research paper is here.  The Washington Post, where the story was taken from, has a graph of the results, and they match the story. Note that this is one of the very few cases where starting a bar chart at zero is a bad idea. It’s hard to roll zero on a standard die.

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The research paper also has a graph of the results, which makes the effect look bigger, but in this case is defensible as 3.5 really is “zero” for the purposes of the effect they are studying

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Unfortunately,neither graph has any indication of uncertainty. The evidence of an effect is not negligible, but it is fairly weak (p-value of 0.04 from 142 people). It’s easy to imagine someone might do an experiment like this and not publish it if they didn’t see the effect they expected, and it’s pretty certain that you wouldn’t be reading about the results if they didn’t see the effect they expected, so it makes sense to be a bit skeptical.

The story goes on to say

These findings have pretty big implications for the workplace. For one, they suggest that the one-size-fits-all 9-to-5 schedule is practically an invitation to ethical lapses.

Even assuming that the effect is real and that lying about a die roll in a psychological experiment translates into unethical behaviour in real life, the findings don’t say much about the ‘9-to-5’ schedule. For a start, none of the testing was conducted between 9am and 5pm.

 

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.

April 4, 2014

Thomas Lumley’s latest Listener column

…”One of the problems in developing drugs is detecting serious side effects. People who need medication tend to be unwell, so it’s hard to find a reliable comparison. That’s why the roughly threefold increase in heart-attack risk among Vioxx users took so long to be detected …”

Read his column, Faulty Powers, here.

December 23, 2013

Meet Callum Gray, Statistics Summer Scholar 2013-2014

Every year, the Department of Statistics at the University of Auckland offers summer scholarships to a number of students so they can work with our staff on real-world projects. We’ll be profiling the 2013-2014 summer scholars on Stats Chat. Callum is working with Dr Ian Tuck on a project titled Probability of encountering a bus.  

Callum (right) explains:

“If you encounter a bus on a journey, you are likely to be exposed to higher levels of pollution. I am trying to find the probability of encountering a bus and how many you will encounter when you travel from place A to place B, taking into account variables such as the time of day and mode of transport.

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“This research is useful because it will give us more of an understanding about the impact that buses have on our daily exposure to pollution. we can use this information to plan journeys and learn more about an issue that is becoming more and more apparent.

“I was born in Auckland and have lived here my whole life. I just finished my third year of a Bachelor of Commerce/Bachelor of Science conjoint majoring in Accounting, Finance, and Statistics, which I will finish at
the end of 2014.

“Statistics appeals to me because it is used everyday in conjunction with many other areas. It is very useful to know in a lot of workplaces, and it is interesting because it has a lot of real-life applications.

“I am going to Napier for Christmas and Rhythm and Vines for New Year. In the rest of my spare time, I will be playing cricket and golf, as well as hanging out with friends.”

 

 

October 8, 2013

100% protection?

The Herald tells us

Sunscreen provides 100 per cent protection against all three types of skin cancer and also safeguards a so-called superhero gene, a new study has found.

That sounds dramatic, and you might wonder how this 100% protection was demonstrated.

The study involved conducting a series of skin biopsies on 57 people before and after UV exposure, with and without sunscreen.

There isn’t any link to the research or even the name of the journal, but the PubMed research database suggests that this might be it, which is confirms by the QUT press release. The researcher name matches, and so does the number of skin biopsies.  They measured various types of cellular change in bits of skin exposed to simulated solar UV light, at twice the dose needed to turn the skin red, and found that sunscreen reduced the changes to less than the margin of error.  This looks like good quality research, and it indicates that sunscreen definitely will give some protection from melanoma, but 100% must be going too far given the small sample and moderate UV dose.

I was a also bit surprised by the “so-called superhero gene”, since I’d never seen p53 described that way before. It’s n0t just me: Google hasn’t seen that nickname either, except on copies of this story.