There are two different methods for reasoning, inductive and deductive. While deductive reasoning looks for certainties, inductive reasoning is used when a circumstance is seen as supplying evidence that a conclusion could be true. The belief in a conclusion based on inductive reasoning is highly probable and based on evidence that can be seen and measured but is not 100% certain. Inductive reasoning is a tool for making well informed educated guesses using all the available data and information you have at the time. It doesn’t mean you will be correct but this type of reasoning will give you the best estimation you can make based on the current known facts. Deductive reasoning is a tool you use when you don’t know something to be a fact 100% or you are having to make the best decision in an uncertain environment.
The different types of inductive reasoning:
Generalization: Drawing a general conclusion about all of a group based on an observation. All the chickens I have seen are white, so all chickens must be white.
Statistical: Make a high probability guess based on statistics. If 90% of all chickens are white then out of ten chickens, nine should be white.*
Sample: Making an educated guess about others based on a sample size. If nine out of ten chickens on this farm are white then 90% of chickens will be white on my neighbors farm.
Analogous: Makes a conclusion based on the shared attributes of two types of things. 90% of chickens are white and ducks are birds like chickens so 90% of ducks are probably white too.
Predictive: Makes a conclusion by using a prediction from a past sample of data. When I went to this farm three years ago 90% of the chickens here were white so when I go back tomorrow 90% of the chickens are likely to be white again.
Causal inference: Makes a conclusion using a causal connection. 90% of chickens on this farm are white. I just saw a white bird in the woods behind the farm. The bird I saw was likely a chicken.
*(All chicken color data used in this post is fictitious.)
Inductive reasoning can be a tool to use for a higher probability of having a correct assumption over randomness. However it can be dangerous when used to see links that are not really there or to assume too much from a limited set of data. In the business, investing, and trading world inductive reasoning must be used a lot of the time because in those environments people must manage so much uncertainty in the future that they can only make the best decisions possible using the available data. The quality of decisions using inductive reasoning are based on the value and real connections of the data to reality.