Inductive and Deductive Reasoning

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Reasoning : the action of thinking about something in a logical, sensible way
 

Earlier this week, a DataCamp course sparked an interesting thought about how we reach conclusions.

In data analysis, we ask key questions, gather information, and draw insights from what we find. This happens across many STEM fields. But something I recently noticed is how we actually get to those conclusions – there are two main methods: inductive and deductive reasoning.

Let's break it down:

Inductive Reasoning:  Imagine you see all your friends going into tech or medicine. Based on this limited view (your friends), you might guess (general conclusion) that young people aren't interested in plumbing careers. However, this might not be entirely true!

Deductive Reasoning:  This approach is more like a detective story. We use established facts (like employment data) and analysis tools to reach specific conclusions. For example, we could analyze data over time to see if there's actually been a decline in people entering plumbing over the past decade.


With that being said, here are some pros and cons of these two types of reasoning: 


Aspect Inductive Reasoning Deductive Reasoning
Pros  Flexibility in exploring new ideas
 Encourages creativity
 Identifies broad trends		 Precision in drawing specific conclusions
Accuracy in verifying hypotheses
Provides clarity and logical structure
Cons Uncertain conclusions based on limited data
Risk of bias in interpreting observations
Limited applicability of conclusions		 Rigid and may overlook exceptions
Limits creativity due to reliance on rules
Dependency on premises for reliability


So, which one to use?

Inductive reasoning is a great starting point for forming ideas or theories.

Deductive reasoning comes in handy when you want to test or confirm those ideas with real data.

Basically, inductive reasoning helps us guess, while deductive reasoning helps us prove (or disprove) those guesses!


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