Incomplete induction is a logical principle which states that because every observed instance of a phenomenon has a certain outcome, then it is reasonable to assume that the same outcome is true for all instances of the phenomenon. This type of reasoning is often employed in everyday life, but it is inherently flawed as it does not take into account the possibility of exceptions. Despite this, incomplete induction is often used to come to conclusions about phenomena which have not been fully explored.
Within the field of Statistics, incomplete induction is more commonly referred to as hasty generalization. This is when a conclusion is drawn from a limited number of observed instances and then applied to the whole population. For example, if a survey of 100 people reveals that 80% of them are in favor of a certain law, it would be wrong to then assume that the majority of the population is in favor of the law or that this is a universal opinion. This is because there is likely to be a much larger population outside of the surveyed that could have a different opinion. The same applies to any type of study.
In science, incomplete induction can lead to faulty theories as hypotheses are based on observations which may not be complete or correct. For example, in astronomy, ancient astronomers assumed that because all planets they had observed moved in a circular motion that all planets must move in a circular motion. This idea was known as the geocentric model and held for over a thousand years before being disproved by Copernicus in the 16th century.
Despite the inherent flaws of incomplete induction, it remains a commonly used method for decision making. For example, an employer may be deciding which employee to promote to a certain position and may only need to consider a few instances of that employees past work as evidence for their candidacy. This could lead to serious and unforeseen consequences, as it does not take into account the employees potential for future growth and development.
Incomplete induction can also be used in business decisions. For instance, if a company has previously only dealt with a certain type of customer, they may be tempted to only pursue those types of customers in the future. This can lead to the company losing out on potential profits, as they are not considering other types of customers that could be interested in their products.
In conclusion, incomplete induction can often lead to false assumptions and faulty conclusions. As such, it is important to always weigh all the evidence and consider the possibility of exceptions before making any assumptions based on incomplete observations. Doing so can help to avoid costly mistakes that could have been avoided if the complete facts had been taken into account.
