Hello and welcome to My 2 Cents! A blog that takes on analytical perspective on subjects that fall under social sciences (mainly psychology) and sports (mainly basketball), and communicates that through data analysis and plots.
This is the first post of the blog so, I would like to start with what analytical (reflective) thinking is since it will be at the core of this blog.
What is analytical thinking?
It (analytical/reflective thinking) stems from a model called Dual-Process Model of the Mind. The model groups cognitive processes into two:
Type I: Intuitive, automatic cognitive processes that require low effort.
Type II: Deliberative, effortful thinking.
Type I (intuitive from now on) thinking corresponds to processes such as walking or brushing your teeth, while Type II (reflective from now on) corresponds to processes such as solving linear system of equations etc., processes that require cognitive effort. The distinction is pretty apparent when you ask your friends the question of "what would be your ranking if you pass the guy in the second place in a race" — they usually give effortful (Type II) right answer after a very quick (Type I) wrong one first.
The model is used widely in different areas as a theoretical framework (political and moral judgments and decision making).
Which one is better? Well, I'm a huge advocate for reflective thinking but I understand the importance of being able to rely on intuition (e.g., any time a surgeon has to make a decision while racing against time). But when it comes to making a, for example, policy decision, one has to be very cautious and has to think through (i.e., deliberately think).
Blocks to our reasoning
We, as humans, are not logic machines. We are affected by the way the information is presented to us (framing), our emotions have a toll on our decision making (although it's not necessarily a bad thing to run as-if experiments, i.e. how would I feel if this outcome occurs, emotion-laden decision making), and we have cognitive biases (http://yourbias.is/) that block our reason. For example, hindsight bias (i.e., knew it all along) is something that I run into very often in my life. I watch basketball games two times a week on average. Occasionally, when a guy makes a shot, the commentator goes "this was a good shot."
This is where the reflection and analytics have to come into play. First step, one has to have knowledge of cognitive biases, such as this one, to be able to identify them. It's apparently a `good shot` after the guy makes it. Next step is to approach it from an analytics perspective and start with the following question: "Can I measure shot selection quality?" Rest is about having the data, tools, and skills to apply statistics intentionally to answer such question (you will see a lot of posts in this blog as a end-product of this process).
So, in this first post I wanted to foreshadow the approach in the upcoming posts. Anytime there is a statement, this blog will be skeptic of it and assume there is more than meets the eye. There will be constant effort to recognize biases, including the data collection processes and reports in articles, and attempt to take an approach that gets us closer to the truth.
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