The “employability skills” debate has been rebooted by the findings of “Learning to grow: what employers need from education and skills”, a CBI/Pearson survey in which 542 organisations employing around 1.6 m people, were asked what they needed from education and skills.
Against a backdrop of a need to reverse unemployment and build the UK’s competitiveness, 30% of employers said they were dissatisfied with the level of literacy and numeracy of school and college leavers. They want primary schools to concentrate on the three ‘r’s and 71% of employers said they want secondary chools to prioritise the development of school leavers’ ”employability skills” and when it comes to graduates (1 in 5 jobs already requires a degree and this proportion is set to increase), 81% of employers say that “employability skills” are worth more than the subject or class of a degree.
So, in addition to being literate, numerate and IT-skilled, what do employability skills comprise? It’s a lot about being work-ready: understanding the type of real workplace issues and problems addressed in different sectors and having business-relevant skills such as problem solving, initiative and self-management, to tackle them.
Statistics – confident data handling and an ability to ’reason’ statistically – are a key part of those skills. Statistics is all about problem solving: applying number skills, finding and communicating the information in numbers in order to make decisions and take action.
Good Statistics teaching uses real data to address real issues, using a problem-solving approach, similar to the paradigm followed by people carrying out scientific inquiry. The resultant problem solving skills are valuable in every workplace.
getstats seconds the views of John Cridland, DG of the CBI who says “It is essential that our schools equip every young person with the attitudes and competencies they need to lead fulfilling and productive lives”. We agree with Rod Bristow, UK President of Pearson, that “young people want their education to help their employment prospects” too. Whilst employers are tackling these issues: more engagement with schools and HE, more apprenticeships and more work-based training to plug skills gaps, on the whole, the figures employers have fed back to this year’s ’Learning to Grow’ survey, are broadly unchanged since the first survey of this kind in 2003….which suggests a major change in our approach to education and training is needed.
We know that employers and higher education (HE) are concerned about the successor generation’s level of statistical know-how and skill. Last year’s Advisory Committee on Mathematics Education’s “Mathematical Needs” found that employers value statistical skills and are concerned that when it comes to numbers, data and statistics, too many young people are out of practice by the time they start their first job. It also found that universities are having to plug major gaps in the quantitative skills of new undergraduates across the sciences and social sciences.
Maths is the subject where students gain a grounding in the basics of Stats so that they can use stats skills in most other subjects across the curriculum. Yet over 300,000 young people leave school without a grade C in Maths. over 80% of students drop Maths at 16 and whilst employers are concerned about the abilities of new recruits, universities report that over 200,000 students who enter HE courses lack the maths and stats skills they need to study subjects from geography, biology to business studies, criminology and social science.
Over the comings weeks and months, our new StatsAtWork column will look at these and related issues more closely: what is the impact of stats skills in different workplaces?, what are the stats skills of the existing workforce? how can staff and employers gauge the extent of their existing stats skills? ……




I don’t know about the UK, but IMO, the basic stat survival skills should be in order of priority:
- Know he difference between various measures of central tendency and when it is unspecified ASK.
- Know how big a difference dispersion makes in any result. If some measure of Dispersion is not specified, ask what it is and know what the various measures mean.
- Know how to tell if graphical results are displayed properly and how the visuals can skew perspective.
- Know what a random sample is and how much impact a biased sample can have on results. At least know when to question the sampling technique (self selected samples for example).
- In surveys, understand how results can be biased by the way questions are asked.
Granted, these are very primitive and necessary to make sure that statistical results are not manipulated in order to satisfy some Agenda.
These are pretty much from the 1950′s book “How to lie with Statistics” and should be mandatory in all education, regardless of country.
Notice I haven’t mentioned anything here regarding how things are calculated or derived. I don’t think those concepts are necessary in order to understand the basic statistical concepts.
Employees with maths not beyond OL/GCSE used not to spend work time being taught relevant maths/stats, but attended classes in their own time which was usually in he evenings at Technical Colleges.
The courses were tailored to cover local needs. Students included Civil Servants, Graduates, Engineers, Lab. Assistants.
Of course working days used to be longer, breaks fewer and holidays shorter, and outside attractions far fewer. Time to spend resolving a maths/stats problem was more easily available.
I don’t know about the UK, but IMO, the basic stat survival skills should be in order of priority:- –
Know the difference between various measures of central tendency and when it is unspecified ASK.
Know how big a difference dispersion makes in any result.
If some measure of Dispersion is not specified, ask what it is and know what the various measures mean.
Know how to tell if graphical results are displayed properly and how the visuals can skew perspective.
Know what a random sample is and how much impact a biased sample can have on results. At least know when to question the sampling technique (self selected samples for example).
In surveys, understand how results can be biased by the way questions are asked.Granted, these are very primitive and necessary to make sure that statistical results are not manipulated in order to satisfy some Agenda.
These are pretty much from the 1950s book ‘How to lie with Statistics’ and should be mandatory in all education, regardless of country. Notice I haven’t mentioned anything here regarding how things are calculated or derived. I don’t think those concepts are necessary in order to understand the basic statistical concepts.