The Flaw of Averages is the tendency for people to use averages or means as a default measure of central tendency, without considering the inherent variability in data. This can lead to decision-making that is based on incorrect assumptions and can ultimately result in poor outcomes.
For example, if a manager were to use the average salary of their employees to make a budget, they might underestimate the actual cost of salaries and end up overspending. This is because the average salary does not take into account the fact that some employees make more or less than the average. In this case, using a more accurate measure of central tendency, such as the median salary, would be more appropriate.
Another example: Imagine that you are a project manager and you are trying to estimate how long a project will take to complete. You collect data on the completion times of similar projects and you calculate the average time. Based on this information, you estimate that your project will take a certain amount of time to complete.
However, this estimate is based on the flawed assumption that all projects have the same completion time. In reality, completion times can vary significantly from one project to another, depending on a variety of factors such as the complexity of the project, the skill of the team, and the availability of resources.
Using the average completion time to make a prediction can lead to suboptimal results, because it ignores the variation and uncertainty in the data. It would be better to use a more sophisticated approach that takes these factors into account, such as using a statistical model to predict the completion time of the project.
These are few examples of how using average values can lead to flawed decision making. In many situations, it is important to consider the variation and uncertainty in the data, rather than just relying on the average value.