You may think the chances of 'tails' on the next flip are higher than before. After fifteen 'heads' in a row, you might feel there must be an end to the pattern. The Gambler's Fallacy, or the Monte Carlo Fallacy, is the belief that if something happens more frequently than normal, it will happen less frequently in the future. In other words, do not use the same information to both construct and test your hypothesis. You can prevent falling for this fallacy by first formulating a hypothesis, and then testing it. This bias is related to the clustering illusion, which is the tendency in human cognition to interpret patterns where none actually exist. It is named after a fictitious sharpshooter who lets off a lot of shots at the side of a barn, looks at it, finds a tight grouping of hits, paints a target around it, and then claims to be a 'great sharpshooter'. In many cases because this subset leads to the most interesting conclusion. The Texas Sharpshooter Bias typically arises where a person has a large amount of data, but only focuses on a small subset of this data.
In this blog, we explore 11 common pitfalls you may encounter during the data analysis process. Analysing data comes with its own pitfalls. However, having identified the data you need, and why you need it, you still may not be out of the woods. The data itself may be incomplete, inconsistent, or unavailable. It may be difficult to collect or identify the right data streams. While working on drawing meaningful conclusions from your data, you are likely to encounter many challenges. However, data analysis is not something to jump into blindly. Whether it be profitability analysis or looking for ways to improve processes, data guides the process.
As we have noted in recent blogs, data collection and analysis is essential to the long term success of your business.
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