Image created with Midjourney. Image prompt: A stork carrying a baby in a bundle, flying over a house filled with books, while a group of students studying with high grades displayed above their heads
In the realm of software development, understanding logical fallacies and cognitive biases can play a significant role in improving the quality and usability of digital products. One such cognitive bias is the concept of "False Causality" - a logical fallacy where two events that occur together are wrongly assumed to have a cause-and-effect relationship1.
False Causality, also known as spurious correlation or non-causal correlation, refers to the perception of a relationship between two variables where none actually exists. This is often due to coincidental correlation or a common cause that is overlooked. A classic example is the supposed correlation between the number of storks and birth rates in a certain region - while there may be a correlation, it doesn't mean storks deliver babies. Similarly, students having better grades when there are many books in their home does not necessarily mean that the presence of books directly leads to better grades. It could be that educated parents, who value their children's education, tend to have more books and also encourage better studying habits in their children1.
When it comes to software development, false causality can occur when making assumptions about software performance based on certain indicators. For example, one might notice that a website has more visitors during a certain period and associate it with a recently implemented feature. However, the increase in visitors could be due to a completely unrelated event, such as a holiday season or a marketing campaign.
Suppose a software company releases a new feature and subsequently notices a boost in user engagement metrics. It would be a case of false causality to instantly attribute the increase in engagement solely to the new feature without considering other factors. Perhaps the company had also ramped up its marketing efforts around the same time, or there was an industry trend that brought more users to the platform.
Another example could be a software company noticing a decrease in customer complaints after implementing a series of bug fixes. They might assume that the bug fixes have directly led to increased user satisfaction. However, the decrease in complaints could be due to other reasons, such as changes in customer support procedures or decreased usage of the application due to seasonal trends.
A final example could be a correlation between website design changes and conversion rates. If a company changes the color scheme of its website and observes a spike in conversion rates, attributing this improvement solely to the new color scheme would be a case of false causality. Other factors, such as a concurrent price reduction or a new product release, could be driving the change in conversion rates.
As these examples illustrate, it is crucial to consider all potential factors and conduct thorough analysis before drawing conclusions in software development. Understanding the concept of false causality can help software developers and product managers avoid making misleading assumptions that could lead to ineffective decision-making. By ensuring all potential factors are considered, one can more accurately determine the true causes of changes in software performance and user behavior, leading to more informed and effective product development strategies.