The world is increasingly data-driven. Large and small companies use data to make decisions regarding all sectors. Although most companies have access to several types of data, these are effective only if companies have the ability to understand and explain what they mean.
Data and information constitute two of the companies most strategic assets and the challenge they face is to interpret and use the available data in a way that positively influences business changes.
The key is allowing the data to tell us a story via detailed reports. These stories explain the data, and they are of paramount importance in enabling business leaders to take an effective action plan. Business decision makers must be able to rely on an accurate story to make the most effective decisions. The faster a company is able to make accurate decisions, the greater its competitiveness and benefits will be. Without a story it is difficult to understand what the data are trying to tell us.
In this increasingly digital world, there are a lot of aspects that tend to create confusion. One of them are the coincidental clusters occurring in every collection of data. The other one is our human perception that tends to identify patterns where they don’t actually exist. This latter is called ‘clustering illusion’. These two aspects combined make people ignore differences and focus on similarities.
This concept introduces us to a very important and common topic: the ‘Texas sharpshooter fallacy’. It is a distortion of cognitive thinking that is committed when differences in the data are ignored while similarities are emphasised, leading to serious mistakes and false conclusions.
Through this phenomenon, every reasoning is stripped of all clues that might indicate our ideas are wrong and the emphasis is placed on the information that seems to support our hypothesis. This can lead to distort our reality in favour of a personal interpretations.
The name ‘Texas sharpshooter’ has a funny origin. The story goes that a Texan, in order to prove that he was an excellent marksman, fired shots at the wall of a barn. He then approached the barn and drew a target right where the closest group of shots had landed. He ignored the shots that had landed in other parts of the barn and claimed to everyone that he was a sharpshooter.
In other words, after carrying out the action, the sniper took the necessary measures to manipulate the results of his actions in order to look ‘victorious’. This story explains in a very simple way how who are afflicted by the Texas sharpshooter fallacy have got actual data, but alter them to confirm their hypothesis. In this sense, this fallacy is part of all those errors arising from the fact that a correlation doesn’t imply causation.
It is easy to fall victim to this type of fallacy, this is the reason why when evaluating data, one must have the patience to analyse them and draw all conclusions without distorting them. The Texas sharpshooter fallacy happens in different fields, from scientific studies to companies reports. Hypothesis in the first case, and data interpretations in the second, are correct until a subsequent one proves the opposite. This produces different consequences such as modification of scientific theories or changes in business plans.
People must always try to think critically about the information they have and the conclusions they have drawn from them. The key point is to explore possible alternative explanations in order to consider all the discrepancies, avoiding randomly dictated correlations.
Written by Riccardo Pandini.
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