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The problem with statistics…

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A statistic is defined as “a fact or piece of data obtained from a study of a large quantity of numerical data”. Its very definition states that it is a “fact”, which in turn is defined as “a thing that is known or proven to be true”. As a result, there is a perception and expectation that statistics are something which can be inherently trusted, in which case this should in theory be the shortest blog post on our website. 

The problem is, it just isn’t that simple.

Statistics

Statistics are and can be an extremely useful tool in explaining and rationalising key situations or circumstances, quickly and simply. This is exactly how they are approached in Western education, in science and math, which corroborates that view. They can often be taken very literally and have ascended to a point where it is assumed that they are honest and somewhat infallible. The problem is, they are also a key tool in the spread of misinformation, and one of the most reliable devices utilised to mislead people.

Statistics: the basics

If we have a bucket containing 10 balls of which 4 are green, 2 are purple, and 4 are half purple, half green, then we can present a series of truths in the form of statistics:

  • 40% of the balls in this bucket are green
  • 60% of the balls in this bucket are not completely green
  • 80% of the balls in this bucket contain the colour green
  • None (0%) of the balls in this bucket are blue

Every one of these points is statistically true, but each one communicates different information about the balls in the bucket. What’s more, you only know the information to be true because of the context they have been placed in – having detailed the contents of a bucket and balls in the first place.

So what’s the problem?

The problem with statistics is that while they can be true, they can change meaning depending on the context in which they are placed, and then there are multiple different ways of interpreting the same results. Balls in a bucket is a relatively straightforward example of statistics and already we can see four separate truthful statements, but here is where the complexity grows. Many statements you read will not focus on this type of statistics, but will present correlations, increases and decreases over time, or information which is factually true in one context, but does not account for information outside of this context.

Dare we enter the realms of the current Vax/Anti-Vax arguments? It is not our desire to wade into the arguments of what you should and should not do with your body, in fact quite the opposite, but where our role does overlap is in the fight against misinformation, and preventing the manipulation and coercive control of someone, one way or another, thanks to misleading statistics.

In the UK, this table (and many of its weekly published counterparts) has been doing the rounds, heralded by anti-vaxxers as proof of the ineffectiveness of the Covid-19 vaccine.

On the surface, the claims are true. The exact number of people in hospital who have been vaccinated is bigger than the number that haven’t. For example, 1,073 people older than 80 are in hospital despite vaccinations, which is bigger than the 208 in hospital without vaccinations. So, many choose to conclude that the vaccination must therefore be ineffective. EXCEPT, what this table entirely omits is the context and clarification of overall vaccine coverage.

Let us explain…

In the UK, significantly more people have been vaccinated than haven’t, which means that those with vaccinations is a large group, and those without vaccinations is a small group. So, if we try to compare the two groups we can’t because we are comparing two groups that are not directly the same, and we therefore have to adjust the numbers so that we are comparing like-for-like. In this instance, we need to adjust the figures to be per 100,000 of the population in each group, so that we can account for the overall difference in vaccinated versus unvaccinated. The British Medical Journal does an excellent job of this, in their regular updates like this one, where they highlight: “For example, between the week beginning Monday 16 August 2021 and the week ending Sunday 12 September [2021], the rate of hospital admissions of over 80s was 50.5 per 100000 in the fully vaccinated and 143.9 per 100000 in the unvaccinated, while deaths were 45.5 and 145.4 per 100000, respectively. These trends were seen across the board. For example, for 60-69 year olds the hospital admission rates were 13.5 per 100000 in the fully vaccinated and 74.3 per 100000 in the unvaccinated, while deaths were 4.1 and 24.3 per 100000, respectively.” So yes, while the numbers in the table above do show one group is bigger than the other, in reality when they are given context to make them directly comparable, there are more people in hospital without vaccinations than with.

So, the essential takeaway here is that you need to clue yourself up on how to interpret statistics and make sure that whatever you are reading, you understand the context of the statistic as well as what that statistic actually represents.

Inspecting statistics

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