Your Shortcut to Critical Thinking: Identifying Flaws in the Argument- Causation, Correlations and False Correlations

Written by Argumentful

Marie Curie, Einstein and Darwin had long hair. They were all great scientists. Therefore, to be a scientist, you need long hair. And don’t attempt to trim it after it has grown or you might lose all your scientific skills and knowledge!

Is this a flawed argument?

And how can it be that people come out with arguments which are erroneous? Well, we need to look at the author’s intentions:

-the argument’s faults have been missed by accident or by not paying enough attention


-the argument includes erroneous elements on purpose

The difference between the two is intention. Either the author intended to bring about a flawed argument knowing very well that it contains false information, or the author has come up with the flawed argument by mistake, missing to check carefully the claims, reasons and evidence supporting them.

It follows that being familiar with the different ways in which an argument can be wrong helps recognize the inaccuracies in the claims we encounter in everyday life, be those intentional or accidental. All this is critical thinking.

In this post we will discuss two of the most common flaws in reasoning- false causation and false correlations.

If two events happen together, we are tempted to assume there is a link between the two. This kind of thinking is erroneous reasoning.

There are two types of associations people who do not exercise critical thinking tend to make whenever two things appear together or two events happen at the same time:

-an assumption is made that one of the events is the cause of the other


-a correlation between the two events is assumed- we might be tempted to think that events with similar trends must be somehow correlated.

Let’s discuss each of these.

Causation and Inferring Causation from Association

Michael Innis wrote an interesting paper published in 2009 showing how phenomenon B is caused by phenomenon A when both of the following conditions are met:

1. Phenomenon A always precedes or accompanies phenomenon B

2. The absence of phenomenon A determines the absence of phenomenon B

Pay attention to the second condition listed above, because this is the one which is overlooked when we make erroneous assumptions about causal links. Consider this example:

Life expectancy is much higher in Western countries than in the past. Internet usage is also much higher. Therefore, internet usage must increase our life expectancy.

Internet Usage= A

Life Expectancy= B

Condition 1: When A is present, B must be present.

Condition 2: When A is absent, B must also be absent.

Here the link doesn’t follow logically from the reason given: the implicit warrant (or general rule) is that the usage of internet leads to longer life expectancy. It hasn’t been proven that those who use the internet live longer compared to those who don’t and there is also no explanation to why the usage of internet might increase life expectancy.

Looking at the argument map, it is quite clear that this assumed causal link is erroneous:

If you find it tricky to spot the false causations, try the following: check that the absence of the cause (A) generates the absence of the effect (B).

In this case, if there was no Internet, would the life expectancy decrease? What about populations that do not use the Internet, do they have a lower life expectancy?

When it comes to wrong causation, sometimes there is association between the phenomena, sometimes there is no connection, such as in the example above. Here not only do we not have causation, we are also dealing with a case of false correlation. (See next section in this article).

But let’s take a look at a more realistic example. A few weeks ago we discussed the report produced by the Office for National Statistics (ONS) which was showing a higher mortality from COVID-19 of Black, Asian or minority ethnic (BAME) people compared to whites.

This report seems to suggest that being part of the BAME group (cause A) will result in an increased risk of dying from COVID-19 (effect B).

The ONS found that black males were 4.2 times more likely to die from COVID-19 than their white counterparts.

Let’s verify condition 2 as described above- Condition 2: When A is absent, B must also be absent.

So, if the person could change their ethnicity, but all other conditions remained the same, they would no longer be under this risk. I am sure you can already see how this reasoning is flawed: the person would still have the same underlying health conditions, social conditions, jobs and other risk factors that are more likely the cause of this increased risk.

In this example we are dealing with correlation: the ethnicity is of course correlated to the increased risk of dying from COVID-19. Nevertheless, correlation does not imply causation. Here it is highly likely that we are dealing with a case of correlation with “third causes”: other factors which are characteristic of BAME population and which are more likely to have increased the risk of death from COVID-19, such as lack of vitamin D, job types or types of accommodation.

Correlations and False Correlations

Correlations appear when trends are related to each other.  So there has to be some kind of mutual relationship between the trends.

Correlations could be positive or negative.

First, let’s take a look at some valid examples of correlations:

Positive: Taller people have larger shoe sizes and shorter people have smaller shoe sizes. (As the height of a person increases, so does the shoe size).

Negative: As you climb the mountain (increase in height) it gets colder (decrease in temperature).

There are two problems that can arise with correlations:

-the first one was already discussed in the previous section: we assume that if there is correlation, there must also be causation.

-the second problem which could arise is that of false correlation: whenever we assume there is a relationship between trends, but in fact that relationship does not exist.

Let’s take a look at an example of false correlation from Stella Cottrell:

“The number of car crimes has increased. There used to be only a few colours of car from which purchasers could choose. Now there is much more variety. The wider the choice of car colours, the higher the rate of car crimes.”

Needless to say that there is no logical line of reasoning which connects the increase in variety of car colours to the increase of car thefts.

The fact that they happen to take place at the same time is just a coincidence.

Many conspiracy theories are based on false correlations. Consider the example which asserts that the increase in 5G masts in Wuhan is correlated to the increase of the COVID-19 cases. This is likely just a coincidence- cities in the US have now many more cases than the official numbers from Wuhan and there are less 5G masts in the land of free.

In conclusion, recognizing the difference between cause and effect, correlation and coincidence is a critical thinking skill worth developing.

If you enjoyed this post, you might also like these articles inspired by implicit assumptions and argument structure:

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