1. Promoting Good Statistical Practices
Roger Stern - SSC, Reading
WMO/FAO training workshop - November 2005

2. PROMOTING GOOD STATISTICAL PRACTICE
Univ of Botswana, 2005
Contents
Understanding the present situation:
The need for (basic) training in statistics
Past training in statistics
Developments in statistical computing
And in statistical analyses
Possibilities for the future
Resources
statistical software (freely available in Africa)
materials to promote good statistical practices
training materials
Spatial analysis
In conclusion
These are exciting times - let’s look forwards not backwards
Start with some stories – relevant to Africa
It is a question of attitude – Rockefeller 2000
Research projects in African Universities – done by MSc students – held back by poor statistical knowledge of students. Many attempts to change this, but a difficult problem. I visited many of the Universities to meet the students, the staff and the statisticians.
One result from the students was – please don’t start reforms at pg level, that is already too late. Change the u/g teaching.
Easy to see the difficulties – large classes, no computers, no money for demonstrators, etc.
Now – BUCS, open consultancy, so funds for MSc students to demonstrate, lab of 60 (2nd hand) computers, all with internet, new teaching – all describptive in year 2. Show some of this.
Show CAST for climatic – electronic textbook!
Excel – needed, because jobs. UK story – led to SSC-Stat – see later
Climatic – why I am here – Instat. – see later
Statistics for government Kenya Polytechnic, (changed their teaching) Univ of Makerere, statistical institute (harder to change) –
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

3. Training in statistics
Univ of Botswana, 2005
Training in statistics
It is difficult to practice good statistics
unless we have had appropriate training
For example seasonal forecasting
Uses PCA
Spatial methods mentioned in this workshop include:
Kriging, and co-kriging
PCA and clustering
When many staff find more basic concepts difficult
Percentiles and return periods – (show CAST as preview)
Standard errors, etc
So they have to accept (advanced) methods in an unquestioning way
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

4. Past training in statistics
Training for (non-statistician) users in the past has been problematical
consequently they fear statistics
and hence also statisticians
Similarly, insufficient soft training for statisticians
consequently they sometimes lack communication skills
and marketing skills
and are often side-lined in important development and research projects
just like Met staff perhaps???
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

5. Common training problems for non-statisticians
Training is dominated by analysis
with little on data management
or on design
A recipe-book approach is used
hence e.g. overuse of irrelevant significance tests
little understanding of principles
Training emphasises hand computation
for understanding (which they don’t get!)
but not needed later
and little experience of computers for statistical work
Presentation is too mathematical
not conceptual
AND often taught by someone who has little interest in the student’s main subject areas
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

6. PROMOTING GOOD STATISTICAL PRACTICE
RESULT!
Users with near universal dislike of statistics
and statisticians?
strong demand for relevant in-service training in statistics
Most of these past weaknesses in training
are the same for statisticians
who can be too pedantic and inflexible in their advice
and are then feared and ignored, where possible, by potential clients
We see later how this can now easily change
for both statisticians
and for others who need to generate and use statistics
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

7. Advances in statistical computing
History
1960’s SAS and SPSS started
A long way back in computer terms
By early 1980’s
Statistics packages well established
Micro-computers appeared – too small for these packages
So lots of other statistics packages
that made the same mistakes as SAS and SPSS a generation earlier
it is easy to write statistical software, but difficult to write good software
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

8. Statistics packages : THEN
In the 1990’s
Standard statistics packages dominant again
compare other types of software
With some additions e.g. Stata
All command-driven
So you had to learn the language (for SPSS, or SAS)
So people and training courses used just one package
Data transfer between packages was difficult
Training courses often confused
learning the package with learning statistics
c.f. data management – learning concepts or learning Access
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

9. PROMOTING GOOD STATISTICAL PRACTICE
A big advance…..
Windows appeared
& EXCEL ruled the world
for better for worse!
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

10. Statistics packages : NOW
All common packages are in Windows
Very similar interface
Like other Windows software
So very easy to learn
And to add to Excel
so you can still keep your “security blanket”
And easy to add another package
hence not so critical what package is used for statistics training
Data transfer has also become easy
Hardly need a training course
for the software
so can concentrate on training in statistics again!
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

11. Advances in statistical analysis
The “estuary model”
ever-increasing unity to the methods
this makes training much easier
if we build a solid foundation
special methods are then seen as such
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

12. PROMOTING GOOD STATISTICAL PRACTICE
Start in 1960’s
In the mountains there were little streams
Regression and
Analysis of variance
These were for normally distributed data
In another valley
parameter estimation was for other distributions, like Poisson and binomial
And leading to another valley
the chi square analysis for categorical data
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

13. PROMOTING GOOD STATISTICAL PRACTICE
Then
In the late 1960’s
Chi-square tests joined with other ways of looking at multidimensional contingency tables
to become log-linear models
In the early 1970’s
log-linear models
joined probit analysis
into the general stream of generalized linear models
that also included ANOVA and regression
for normal and non-normal data
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

14. PROMOTING GOOD STATISTICAL PRACTICE
And finally for us here
In the 1980’s
REML started
and is for data at multiple levels
By the 1990’s it had joined the mainstream
and included powerful methods for spatial modelling
So now
same modelling ideas used for a wide range of problems
Making both training and analysis
simpler and more coherent
as long as the trainers know.
BUT some are still up in the mountains!
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

15. PROMOTING GOOD STATISTICAL PRACTICE
So where are we now?
Statistical software has developed
and so has user’s computing skills
Statistical methods have developed
and are easier to use
And the resources to bring the two together
are now being made available
and are becoming accessible throughout
We describe some of these resources
First generally
And then look briefly at methods for spatial modelling
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

16. PROMOTING GOOD STATISTICAL PRACTICE
Software includes:
SSC-Stat
add-in for Excel to encourage good use
with a tutorial guide
and guides for good tables and good graphs
for example it provides boxplots
Instat+
first simple statistics package for ‘Excel-lers’
supports good teaching of statistics
stepping stone to other statistics packages
tutorial guide, introductory guide
and climatic guide, now updated for Instat Version 3
for example for data summary or training
Genstat
One of the major statistics packages (like SPSS, Systat)
For modern statistical modelling, like GLMs and REML
And good facilities for spatial modelling
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

17. PROMOTING GOOD STATISTICAL PRACTICE
Excel Add-In
A simple add-in developed by SSC, Reading University
To make EXCEL more effective for simple data processing
Can be installed like other add-ins to ordinary EXCEL
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

18. PROMOTING GOOD STATISTICAL PRACTICE
SSC-Stat
Descriptive statistics using SSC-Stat
e. g. parallel box plots,
To show outliers, means and medians for a variate, split by different factors
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

19. PROMOTING GOOD STATISTICAL PRACTICE
INSTAT+
Simple statistics package, developed by SSC, Reading
To facilitate training in good statistical practice
Also provides a painless way of preparing for a move to a major statistical package
Special facilities for dealing with (daily) climatic data
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

20. PROMOTING GOOD STATISTICAL PRACTICE
Instat
A graph from Instat
Showing climatic summaries
A useful aid to exploring statistics concepts
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

21. PROMOTING GOOD STATISTICAL PRACTICE
Instat – for training
Instat using graphs to explore distributions
emphasizing the need to understand the underlying structure
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

22. PROMOTING GOOD STATISTICAL PRACTICE
Genstat
Specially for agricultural applications
And now with added climatic features
Like extremes, and circular plots
Plus a climatic guide
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

23. Modern Statistical Methods
Dialogue from GenStat
Showing an easy way to handle more general statistical models
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

24. Resources for good statistical practice
Good practice guides
Mini-guides for statistical sceptics
designed originally to promote good statistical practice in DFID projects
covering design, data management analysis and presentation
a book is now available
And so much more:
Participatory (QQA) stuff, important for Met services
Now a book is available, based on Malawi’s “starter pack”
Data management – where Met services can support other groups
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

25. PROMOTING GOOD STATISTICAL PRACTICE
Links (for Slide 23)
Good Practice Guides:
Can be viewed by adding a link to the Instat cd or live through the SSC web site
The Green Book:
add the link to the Green Book cd
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

26. PROMOTING GOOD STATISTICAL PRACTICE
QQA
Bringing together quantitative and qualitative data together in a meaningful way
Based on work in Malawi
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

27. Training resources include
Statistical games to help teach statistics
Reading and BUCS
For example PADDY, the rice survey game
Materials for distance learning
Now CAST in general
But can now be adapted for African needs
With support from the Rockefeller foundation
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

28. Interesting ways of learning Training software
Statistics concepts through CAST
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

29. Interesting ways of learning Training software
Statistics concepts through CAST
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

30. Interesting ways of learning Training software
Statistics concepts through CAST
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

31. Interesting ways of learning Statistical Games
Simulating a survey based on a real crop cutting survey in Sri Lanka
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

32. Interesting ways of learning Statistical Games
Simulating a survey based on a real crop cutting survey in Sri Lanka
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

33. PROMOTING GOOD STATISTICAL PRACTICE
And in climatology
Providing the basic statistical skills
Now through a facilitated e-learning course
Tested in 2005, and provided from 2006
For staff in HQ and (hopefully) in outstation offices
Because decentralisation is important
Using a specially adapted version of CAST
That can be provided to African Services
You have seen this earlier
Also software (Instat) plus Genstat
Each with their special climatic guide
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

34. PROMOTING GOOD STATISTICAL PRACTICE
Spatial ideas
More to spatial analysis than just maps
Remember the data – when will you map?
Daily – many “layers”
Annually (e.g. date of start of the season)
Averages – take care of different years at different stations
Example where map does not give the full answer
Southern Zambia – risky for maize
Suggest strategy – say farmers overall have 20% (1 year in 5) risk of replanting
How much seed should be stocked?
Map – very simple 20% everywhere – does it answer the question?
Need spatial correlations – why?
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

35. PROMOTING GOOD STATISTICAL PRACTICE
GIS and mapping
Many problems can be mapped effectively
Then much “spatial analysis” is descriptive statistics
Selection of subsets,
Transformations to provide new layers
Logical calculations
Etc
This is non-controversial
Simple smoothing to provide contours is the same
As long as the spatial “averaging” e.g. splines, inverse distance is recognised as such
But kriging, etc is moving into inferential ideas
And statistical packages could also be used for such operations
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

36. Spatial statistics with statistical software
Many statistical packages, e.g. Genstat
Provide some facilities for spatial analysis
For example kriging
And REML – for the future
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

37. PROMOTING GOOD STATISTICAL PRACTICE
Demonstration
Show two examples of Genstat
First is a simple contour plot
Shows the value of a log file of commands
Second is an example of kriging
Shows more facilities in fitting and plotting
Other facilities include
Co-kriging
REML for “proper” spatial modelling
Within which kriging is a special case
More “research” and case studies are needed
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

38. PROMOTING GOOD STATISTICAL PRACTICE
In conclusion
The time is right:
Statistics has changed
Training methods can change
The resources are here
And in Africa:
Evidence-based decision making is (more) encouraged
Met Services are key organisations
Because climatic data are needed in so many applications
Challenge: How will you proceed??
WMO/FAO Training workshop, November 2005
PROMOTING GOOD STATISTICAL PRACTICE

39. Univ of Botswana, 2005
Thank you