information training for the NHSVisualizing Statistics.The Green RoomThe Bridgewater HallLower Mosley StreetManchester M2 3WS ndTuesday 22 March 2011£250+VAT per placeKurtosis | 99 Giles Street, Edinburgh EH6 6BZ | Tel 0131 555 5300 | email firstname.lastname@example.org| web http://www.kurtosis.co.uk
This training course is called Visualizing Statistics? What’s thevisualizing part of it?It was an accident. We didn’t have “Visualizing” in the title to startwith; it was originally called Vitalizing Statistics. Yes, I know, auseless name for a course. Anyway, every time people askedabout the course they mis-read it and called it VisualizingStatistics instead. So the new name stuck.How much visualizing goes on in the course?A surprising amount, given that the name came about byaccident. We start by assuming that the only way you’ll really getstatistical data across to NHS managers and clinicians is byusing graphical techniques, by drawing pictures, so as well asteaching the calculations, the technical stuff, this course devotesa lot of time to teaching ways of visualizing the information thatyou produce.
What does the course cover?Some basic stuff to start off: means, medians, percentiles,boxplots, that kind of thing. Then we move on to the normaldistribution, standard deviation and standard error. We show youto calculate confidence intervals for parametric and non-parametric data, including an important section that shows youhow to calculate confidence intervals for the differences betweenmeans and proportions. Hypothesis testing and P-values. Finally,a short section on correlation and scatterplots.How did you decide on that selection of material?We did a consultation a few years ago. We asked a few NHSinformation managers what they thought the “essential statisticssyllabus” should be. But we’ve developed the material over theyears as we’ve gained more experience of the differentworkplaces of health service analysts.
Could you be accused of trying to pack too much material into aone-day course?In a way, we could be accused of not including enough! As it is,we have to make sure participants are aware of the limitations ofwhat they learn on the course. Some of the techniques can onlybe used for large samples. Some of the techniques can only beused if your data is normally-distributed. That kind of thing. Sowe have to point people to a Further Reading list if they want—forexample—to explore using the t-test or if they want to trytransforming their data. It’s a difficult balancing act becausethere’s a clear demand for one-day training courses, even whenpeople know that you can’t really cover everything on one day.Yes, but don’t some participants struggle with the amount of stuffthey have to learn in just one day?We’ve found that the vast majority of analysts can cope with it.
Can the course be done by non-analysts?Yes, if they are comfortable and fluent with Excel. And if they areconfident in their numeracy skills.Do you ever find that—amongst NHS analysts—there is aresistance to the idea of learning about statistics?Yes! I think there are a few reasons why analysts are wary ofusing statistics in the NHS workplace. One is to do with difficulty.Statistics is a difficult subject to master. And even if you’vemastered one relevant bit of it, you’ll find that it’s difficult toexplain it to your audience.Ah yes, explaining statistics to laypeople. Do you cover that onthe course?Explaining is the big theme running through the day. There’sactually no point in quoting a P-value, or drawing a chart withconfidence intervals, if you are unable to explain what it means.
So this course is about teaching analyst how to explain statisticsto non-specialists?Pretty much, yes. I mean, of course, we are teaching the conceptsas well, but we are adamant that analysts have to have a deepenough understanding of the techniques for them to be able toanswer any awkward questions that people might ask.How do you go about teaching that? How do you teach people toexplain?Well, it’s a practical, hands-on, exercise-based course. Everyonehas a laptop in front of them and they have to do exercises basedon the learning. And when they’ve completed each exercise, wediscuss it as a group, and the course leader plays devil’sadvocate, you know, the inquisitive manager who asks “daft”questions, or the cynical consultant who asks loaded questions.And we work out strategies for dealing with these situations.
You mentioned the various types of resistance that analysts haveto statistics. You talked about the problem of difficulty. Are thereother reasons for resistance?Inferential statistics is always taught using the concept ofsampling at its heart. For very good reasons. But a lot of analystshave trouble getting their head round the idea of sampling. Theysay that they hardly ever deal with samples; instead, they’ve gotall the data. They don’t need to estimate last year’s averagelength of stay based on a sample, because they know what lastyear’s average length of stay actually was, based on all of thedata.So why should we take sampling seriously?Because if you take a step back you realise that even when wehave all of the data, we are usually still making inferences basedon samples.
You’re going to have to explain what you mean…Well, when we quote last year’s average length of stay for—say—emergency medical admissions, we are often implicitly sayingthat next year’s will be the same. We’re making an inference ofwhat will happen next year based on a sample that was actuallyall of last year’s data.Unless something changes…Well yes indeed. Unless something changes. Which it often does.And statistics has something extremely valuable to bring to theparty here, because statistics tells you whether any change thathappens has arisen by chance, as part of just random variation,or whether it’s a real difference, a significant difference. If lastyear’s average length of stay was 8.2 days and this year’s is 7.8days, we need to be able to tell managers and clinicians whetherthat reduction of 0.4 days was significant or not.
Are you saying that most information analysts don’t do thisalready? They don’t tell whether a difference is a real one or not?By and large, yes. It’s not something that’s routinely taught toanalysts. And even those analysts who know how to do it, theydon’t often think of applying that knowledge to NHS situations.That’s another theme running through the course: the applicationof the techniques to real health service situations. We make surethat all of the teaching examples and exercises use real NHS dataof the kind that course participants will be using as part of theirday-to-day jobs.What kind of examples do you use?A&E waiting times, percentage of patients discharged home froman admissions ward, comparing 7-day re-admission ratesbetween consultants, waiting times of patients attending forroutine outpatient appointments. That kind of thing.
What do participants say about the course? Have you got anyhappy customer feedback that you can share with us?We’ve had some pretty good comments written on our evaluationquestionnaires over the years. One of my favourite commentswas: "Enjoyed it a lot and my brain went "zoom" on all the thingswhich I can apply in current projects.” Another was: "It has givenme a jolt of interest in statistics and enabled me to rememberpast knowledge learnt. Also, much of the course really related towhat I do in my department, which was refreshing and madeeverything connect together." Both of these comments suggestthat people can not only leave the course in a position to applythe learning to their work but it also gets them to develop theirinterest in statistics themselves. If people go out and buy a statstextbook a few days later, that makes me feel a lot better aboutthe world!