Free Critical thinking for Students by Roy van den Brink-Budgen Page B

However, the French figure doesn’t include the 20 per cent of cigarettes sold illegally, so it looks lower than it really is. This last example shows that a per person rate can be distorted by inadequate information. A further illustration is that of India which barely registers on the per person cigarette smoking scale. It comes in as 119th out of 123 countries surveyed. However, tobacco consumption is much higher than this evidence suggests, in that people in India have a fondness for chewing tobacco rather than smoking it.
     
    So we can see that the way in which statistical evidence is presented can affect its possible significance. It will not surprise us, then, to see that such evidence is often used to serve an author’s interest. This can be done, for example, by picking a particular year as the starting point for a percentage change comparison in order to produce an artificially low or high change. It can be done by ignoring (or playing down the significance of) counter-evidence, a method called ‘cherry-picking’. This could be going on with the climate change debate, with different pieces of statistical evidence being used on both sides, such that there is evidence to support the claim that climate change is happening, and that it isn’t.
     
    Sometimes numbers can have a manufactured significance. If we look at the numbers of deaths of troops in military campaigns, what significance do they have? At the Battle of the Somme in 1916, something like 432,000 British troops were killed. Something like 500,000 German troops were killed. Was this more than expected? Was this an acceptable number? In recent military campaigns (such as in Afghanistan), a casualty rate of more than one a day is seen as significant (and even one a day is). In The Times of 29 September 2009, Martin Barrow refers to ‘appalling casualties’ in the war in Afghanistan. How would he then have described the casualty rate of the Somme or at the battle for Stalingrad? What casualty rate wouldn’t then appal? No casualties at all? Just ten or twenty?
     
    Beware of the manufactured significance of numbers. Much of what is reported in the news has this significance. Numerical claims, like other claims we’ve been looking at throughout the book, take on a significance only when something is done with them.
     

 
     
     

EVALUATION OF ARGUMENTS: WEAKNESSES IN REASONING
     
    In the previous chapter, we focused on the limits (and possibilities) of numerical evidence for inference. We established the point that inferences are normally only probably rather than certainly true. In this chapter we’re going to continue this theme of evaluating the relationship between claims and inferences from them.
     

CORRELATION AND CAUSATION
     
    In their book The Joy of Laziness (2005) Peter and Michaela Axt consider the example of Jim Fixx who was the pioneer of jogging. Before Jim Fixx, people who you saw running in the street were normally late for something. The line in Forrest Gump when Forrest says, ‘I just felt like running, so I ran’, would have made little sense to the pre-Fixx era (which was, of course, the point). So why is Jim Fixx being discussed in a book entitled The Joy of Laziness ? Because he died at the age of 52.
     
    You can perhaps already see the connection. Jim Fixx does a lot of jogging, and drops dead at the relatively young age of 52, therefore jogging is bad for your health. It’s an example of R+R→C.
     
    Straightforwardly, the Axts are seeing jogging as at least highly relevant to Jim Fixx’s death. They make the point that this sort of exercise is supposed to give some protection against heart disease, but hadn’t with Fixx. (He died of a heart attack just after a race.) In that we’ve already considered the issue of the alternative explanation, you will have seen that here we have a good example. The authors have assumed that there is no alternative explanation beyond jogging for Jim’s death. But you could, no