A one-day training course scheduled for Tuesday 22nd March 2011 at the Bridgewater Hall, Manchester

1. information training for the NHS
Visualizing Statistics.
The Green Room
The Bridgewater Hall
Lower Mosley Street
Manchester M2 3WS
nd
Tuesday 22 March 2011
£250+VAT per place
Kurtosis | 99 Giles Street, Edinburgh EH6 6BZ | Tel 0131 555 5300 | email info@kurtosis.co.uk| web http://www.kurtosis.co.uk

2. This training course is called Visualizing Statistics? What’s the
visualizing part of it?
It was an accident. We didn’t have “Visualizing” in the title to start
with; it was originally called Vitalizing Statistics. Yes, I know, a
useless name for a course. Anyway, every time people asked
about the course they mis-read it and called it Visualizing
Statistics instead. So the new name stuck.
How much visualizing goes on in the course?
A surprising amount, given that the name came about by
accident. We start by assuming that the only way you’ll really get
statistical data across to NHS managers and clinicians is by
using graphical techniques, by drawing pictures, so as well as
teaching the calculations, the technical stuff, this course devotes
a lot of time to teaching ways of visualizing the information that
you produce.

3. 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 normal
distribution, standard deviation and standard error. We show you
to calculate confidence intervals for parametric and non-
parametric data, including an important section that shows you
how to calculate confidence intervals for the differences between
means 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 NHS
information managers what they thought the “essential statistics
syllabus” should be. But we’ve developed the material over the
years as we’ve gained more experience of the different
workplaces of health service analysts.

4. Could you be accused of trying to pack too much material into a
one-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 of
what they learn on the course. Some of the techniques can only
be used for large samples. Some of the techniques can only be
used if your data is normally-distributed. That kind of thing. So
we have to point people to a Further Reading list if they want—for
example—to explore using the t-test or if they want to try
transforming their data. It’s a difficult balancing act because
there’s a clear demand for one-day training courses, even when
people know that you can’t really cover everything on one day.
Yes, but don’t some participants struggle with the amount of stuff
they have to learn in just one day?
We’ve found that the vast majority of analysts can cope with it.

5. Can the course be done by non-analysts?
Yes, if they are comfortable and fluent with Excel. And if they are
confident in their numeracy skills.
Do you ever find that—amongst NHS analysts—there is a
resistance to the idea of learning about statistics?
Yes! I think there are a few reasons why analysts are wary of
using statistics in the NHS workplace. One is to do with difficulty.
Statistics is a difficult subject to master. And even if you’ve
mastered one relevant bit of it, you’ll find that it’s difficult to
explain it to your audience.
Ah yes, explaining statistics to laypeople. Do you cover that on
the course?
Explaining is the big theme running through the day. There’s
actually no point in quoting a P-value, or drawing a chart with
confidence intervals, if you are unable to explain what it means.

6. So this course is about teaching analyst how to explain statistics
to non-specialists?
Pretty much, yes. I mean, of course, we are teaching the concepts
as well, but we are adamant that analysts have to have a deep
enough understanding of the techniques for them to be able to
answer any awkward questions that people might ask.
How do you go about teaching that? How do you teach people to
explain?
Well, it’s a practical, hands-on, exercise-based course. Everyone
has a laptop in front of them and they have to do exercises based
on the learning. And when they’ve completed each exercise, we
discuss it as a group, and the course leader plays devil’s
advocate, 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.

7. You mentioned the various types of resistance that analysts have
to statistics. You talked about the problem of difficulty. Are there
other reasons for resistance?
Inferential statistics is always taught using the concept of
sampling at its heart. For very good reasons. But a lot of analysts
have trouble getting their head round the idea of sampling. They
say that they hardly ever deal with samples; instead, they’ve got
all the data. They don’t need to estimate last year’s average
length of stay based on a sample, because they know what last
year’s average length of stay actually was, based on all of the
data.
So why should we take sampling seriously?
Because if you take a step back you realise that even when we
have all of the data, we are usually still making inferences based
on samples.

8. 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 saying
that next year’s will be the same. We’re making an inference of
what will happen next year based on a sample that was actually
all 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 the
party here, because statistics tells you whether any change that
happens has arisen by chance, as part of just random variation,
or whether it’s a real difference, a significant difference. If last
year’s average length of stay was 8.2 days and this year’s is 7.8
days, we need to be able to tell managers and clinicians whether
that reduction of 0.4 days was significant or not.

9. Are you saying that most information analysts don’t do this
already? 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 to
analysts. And even those analysts who know how to do it, they
don’t often think of applying that knowledge to NHS situations.
That’s another theme running through the course: the application
of the techniques to real health service situations. We make sure
that all of the teaching examples and exercises use real NHS data
of the kind that course participants will be using as part of their
day-to-day jobs.
What kind of examples do you use?
A&E waiting times, percentage of patients discharged home from
an admissions ward, comparing 7-day re-admission rates
between consultants, waiting times of patients attending for
routine outpatient appointments. That kind of thing.

10. What do participants say about the course? Have you got any
happy customer feedback that you can share with us?
We’ve had some pretty good comments written on our evaluation
questionnaires over the years. One of my favourite comments
was: "Enjoyed it a lot and my brain went "zoom" on all the things
which I can apply in current projects.” Another was: "It has given
me a jolt of interest in statistics and enabled me to remember
past knowledge learnt. Also, much of the course really related to
what I do in my department, which was refreshing and made
everything connect together." Both of these comments suggest
that people can not only leave the course in a position to apply
the learning to their work but it also gets them to develop their
interest in statistics themselves. If people go out and buy a stats
textbook a few days later, that makes me feel a lot better about
the world!