Monthly Archives: April 2019

Book review of “Practice Perfect: 42 Rules for Getting Better at Getting Better”

Book introduction:

“Practice Perfect,” as the title hinted, is a how-to method book. I really enjoyed it and highlighted many notes. However, it couldn’t be half-long. The book chapters are not well-organized. Lemov, the author, breaks the core concepts(rethinking of practice, how to practice, using modeling, feedback, culture of practice and post-practice) into smaller and repetitious portions so that I could not connect every chapter in a straight way.

Below were some of the rules I felt applicable in this book:

Rethink practice and isolate your skills:

Practicing in a hustle and bustle environment sometimes distracts me from recognizing I’m learning in the wrong way. So I participate in the learning workshops or study groups for getting feedback from the other members. I could re-examine what skills to be sharpened through rethinking. Then I break the difficult paths for isolated skill development.

Bright spot:

Focus on your strength. Improving your weakness is a waste of time.

In the past, I tried to learn different domain knowledge and skills for the sake of proving myself. However, these skills didn’t turn out to be a competitive work advantage. As a knowledge worker, you must make effective contributions to your cooperation.

However, honing my weakness only makes me a mediocre worker. Why do I spend more energy    on my strength?

Shorten the feedback loop:

Implementing a small and easy change right away can be more effective than a complex rewiring a skill. So I usually try to post my thoughts on my Facebook after reading.

Replace your purpose with an object:

I love to make a New Year’s resolution to improve myself over the next months. Unfortunately, after only a few short weeks, most of them failed. By writing a reflective journal, I know it’s not realistic. So I adjust my goals to make them specific and measurable. For example, I want to speed up my writing, so I use Excel to record my word count every day.

How to make a better decision(book review of “The Signal and the Noise”)

Which animal would you think defines a good forecaster, fox or hedgehog?

The hedgehog knows one big thing, but the fox knows many little things.

If an original method is not surely working, the hedgehog is reluctant to change, but the fox is tolerant of complexity and is adaptable to find a new approach. That is why the author suggests being foxy is a right attitude toward a good forecaster.

We live in a world in which information is pervasive so that the gap between what we know and what we think we know is widening.  As the study has shown, even the experts usually make incorrect predictions. For example, the probability of the skyscraper being crashed into by the terrorists is 0.05%. The possibility would rise to 38% given that the first building is under attack.

Today’s Forecast is the First Forecast of the Rest of Your Life

If we could use one of the principles, “Today’s Forecast is the First Forecast of the Rest of Your Life” in this book, we could make a better forecast possible today—regardless what we said yesterday, last month, or last year–   and prevent the formidable catastrophe from happening.

Other suggestions the writer proposes are below:

  • Think probabilistically: Acknowledging the real-world uncertainty in our forecast.
  • Look for consensus: It’s not easy to be objective. Other options could help us see the world in different viewpoints to reduce biases.
  • Weighing qualitative information– accounting for the qualitative information along with quantitative factor

This book is a little long but readable, not a formula-heavy, general science book. It consists of four sections.

The first section considers the failures of predictions in finance, baseball, and politics. Then, the author gives the readers some advice about how we can apply our judgment to the data without succumbing to the biases. The second section focuses on dynamical systems (weather, economy, earthquake, and economy) that make forecasting more difficult. Following the third section, it turns toward a solution by an introduction of Bayer’s theorem. Finally, the discussion of applying Bayer’s theorem to more existential types of problems.

Other suggestion:

If you’re interested in general science books or statistically forecasting, please enjoy it. However, if you are the audience who need depth in measuring and making data-driven decisions, you might not appreciate this book as much. I would suggest to read “How To Measure Anything” by Douglas W. Hubbard.