The launch of a new independent charity last week, National Numeracy, is a welcome contribution to tackling the crisis of innumeracy in the UK with 17m adults lacking this key capability of employability. Its stated aim is “to tackle the negative attitudes to maths”.
‘I don’t do maths’ is often said as a badge of pride, as though it excuses any personal responsibility. Yet, as job seekers, you say it at your peril. As Chris Humphries, chair of the charity, says “for business, maths is essential”.
This can be at any level. For example, take the crane operator who has to determine whether he can lift a particular load by making a split second estimate in his head based on weight, height, cable tension etc (the ability to do this was a required competence by employers); or the person in a warehouse who can calculate stock levels in an instant (the ability to do this led to one individual being noticed early in his career and he is now a company director in the same field).
These are about making calculations. However, this is the tip of a bigger iceberg about the importance of numeracy that underpins everyday life and the implications of being employable.
Here are 11 examples of numeracy related to employability based on the brilliant work of mathematician, John Allen Paulos:
- Personalising: The number of people who die in airplanes v who die in car crashes – a common response to the former by the innumerate is to personalise (‘but what if you’re that one?’). The number of people who will apply for this job v you getting an interview or the job itself (‘why would it be me?’). Why not you?
- Probabilities: The probability of getting two heads in two flips of a coin is 1 in 4. A 20% chance of rain implies an 80% chance of no rain. If 2500 people apply for a graduate scheme of 12 spaces, you have a 1 in 191 chance of getting it (0.5%). If 80 people are interviewed, it’s nearly 1 in 30 (3%) at the outset. If 12 people are selected, it’s nearly a 1 in 7 chance (14%) at the interview stage.
- Coincidences: Innumerate people tend to underestimate the frequency of coincidences and endlessly try to rationalise things that seem to correspond. If you anticipate another person’s thoughts or have a dream that seems to come true, many would put this down to the mysterious workings of the universe. The reality is that an unlikely event is likely to occur (the train gets cancelled), whereas there is much less chance that a particular one will (the 7.20 on the day of the interview).
- Chance: The value or opportunity presented to you by the contacts you make on LinkedIn are based on the idea of the number of degrees of separation between you and other people in a chain of connections. So, you might have a modest 60 contacts which translates to a potential 7900 connections two degrees away (friends of friends) and 771,200 connections three degrees away (friends of their friends). Therefore, the likelihood of you and a stranger meeting say at a business conference, and being linked via two intermediaries in between is unexpectedly high.
- Significance: Statistical significance is not the same as how we use the word in everyday language. Through calculating the standard deviation, significance means we are confident that something cannot be down to chance alone. It doesn’t tell you why. The most common error is for an organisation to say, for example, that their productivity or profits or the arrest rates for burglary have gone up by 10% this year without giving any context. On the face of it, you think it is good news. Then you discover that the year before it had gone up 40% and the year before that 2% and so on. Without looking at the trend (and then the underlying causes), it is very difficult to draw any meaningful conclusions. Yet, that’s what newspapers, politicians, and companies do regularly. Understanding significance is significant.
- Magnitudes: The difference between a million and a billion (e.g. it takes 11.5 days for a million seconds to elapse v 32 years for a billion seconds to tick away). Think bankers’ bonuses or the size of the national debt. One graduate chasing on average 100-120 posts (Association of Graduate Recruiters, 2011) v 20% of recent graduates actively seeking work (Office for National Statistics, 2011)
- Scaling numbers up or down proportionally is often invalid. If the number of available jobs goes up by 5%, it doesn’t mean 5% of unemployed people will apply or be able to apply. If the size of a company doubles, it doesn’t mean the size of its departments will also double in size.
- Regression to the mean: If you perform well for a period, it will be followed at some point by a drop in performance. If you have great success for a number of years in your career, it will be followed by a dip at some point. It doesn’t mean you’re no good anymore. Second books for authors and movie sequels are often not as good as the original. They are just regression to the mean performance.
- Filtering: Employers in many sectors usually focus upon winners and extremes. They tend to put people down by comparing them with extraordinary cases (‘he’s not as good as Wayne Rooney’). In baseball, both the Oakland A’s and The Boston Red Sox became highly successful teams by using this insight to their advantage by recruiting players who were perceived by other teams (and therefore overlooked) as average players at knockdown prices. They identified what roles or contributions were inadequate or missing from the team and selected players who had those specific talents even if the rest of their game was ordinary. What particular hidden talent do you have employers need that might have been overlooked by you or others in the past?
- Luck: The difference between the number of heads and the number of tails tends to get bigger as we continue to flip the coin and the changes in the lead from head to tail and vice versa tend to become increasingly rare. Unsurprisingly, some people feel like they are losers and others winners, although there is no real difference between them other than luck.
- Logic: Faulty logic is a kind of innumeracy. Not being able to conclusively refute a claim does not constitute evidence for them. Think Iraq and WMD. Somewhere in here comes whatever you deem to be informal logic, better known as your common sense. An employer might say you don’t have sufficient experience in a particular area. Just because you don’t have it written down on your CV, doesn’t mean you don’t have other relevant experience. One answer is to think more laterally and talk about transferable skills with examples.
And if you’re still confused, remember Einstein:
Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted.