The Drunkard’s Walk, a book worth reading twice, or more. A Data Analyst perspective

As you know, if you follow my LinkedIn profile (which, if you doesn’t, you should) I was reading Leonard Mlodinow’s “The Drunkard’s Walk”, a really great book if you want to get a grasp on how chance, or how randomness, affects our lives, and, what is better, how we fail to perceive what is chance, and what is not.

This book builds, chapter by chapter, on how probability and statistics come to become a thing, and how our understand of this field still bother us, since we have a really hard time ceasing from using our intuition.

Here are some key points that I think it is worth knowing as a Data Analyst. Please, note that those are only some that I remember by heart after just having read the book, either way, I highly recommend this book to anyone that consider finding patterns in data an interesting thing.

  • The probability that two events will both occur can never be greater than the probability that each will occur individually. This is known as The First Law of probability.
  • If two possible events, A and B, are independent, then the probability that both will occur is equal to the product of their individual probabilities. This is known as The Second Law of probability.
  • Girolamo Cardano and the Law of Sample Space
  • Law of Large Numbers
  • Pascal’s triangle
  • Gambler’s Fallacy
  • Bayes’ theorem
  • The bell curve, normal distribution, sample variance
  • Significance testing
  • Confirmation bias, Ambiguous evidence

Those are all really complex topics to explore, so if you feel like knowing more about each of them I highly encourage you to read the book and/or reading on the internet.

Published by Pedro Carvalho

Apaixonado por análise de dados e Power BI

Deixe uma resposta

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: