About this video

• Video Title: How to Improve Your Logical Reasoning Skills
• Channel: Phil McKinney's Innovation Studio
• Speakers: Phil McKinney
• Duration: 00:29:01

Overview

This video explains the crucial difference between deductive and inductive reasoning, highlighting how misapplying these can lead to misinformation and poor decision-making. It offers practical strategies and exercises to improve logical reasoning, enabling viewers to better analyze information, calibrate their confidence, identify contradictions, and question samples, ultimately leading to more effective thinking and decision-making.

Key takeaways

• Distinguish Deductive and Inductive Reasoning: Deductive reasoning moves from general principles to specific conclusions with certainty, while inductive reasoning moves from specific observations to probable general conclusions. Confusing these leads to errors.
• Label Your Reasoning Type: Actively identify whether a conclusion is based on deductive certainty or inductive probability to gain control over your thinking. A daily reasoning journal can help track this.
• Calibrate Your Confidence: Match your certainty level to the strength of the evidence. For inductive conclusions, assign a probability percentage (never 100%) and use language that reflects this uncertainty.
• Hunt for Contradictions: Actively seek evidence that contradicts your conclusions to combat confirmation bias. This helps ensure you're not just looking for information that supports what you already believe.
• Question the Sample: Always inquire about the sample size and representativeness of observations used to support a claim. Small sample sizes or unrepresentative samples often invalidate inductive arguments.
• The One-Word Test: Replace absolute language ("always," "never") with probabilistic terms ("usually," "rarely") and definitive language ("certainly") with tentative terms ("probably," "likely") to train your brain for probabilistic thinking and appear more credible.
• Practice Regularly: Implement strategies like journaling, contradiction hunts, and analyzing claims from social media to make critical thinking habitual.

Deductive reasoning moves from general principles to specific conclusions with absolute certainty, meaning if the premises are true, the conclusion must be true. Inductive reasoning, on the other hand, builds from specific observations towards general principles with varying degrees of probability; it infers a likely explanation based on observed patterns, but the conclusion is not guaranteed.

To use a "daily reasoning journal" for improving logical reasoning, you should write down three conclusions you made that day about people, work, news, social media, or texts you received. For each conclusion, identify the evidence that led to it. Then, label whether you used deductive reasoning (general rules applied to specifics) or inductive reasoning (patterns from observation). This practice creates conscious awareness of your reasoning processes, helping you recognize when you're treating inductive guesses with deductive certainty.

To calibrate confidence, you need to match your certainty level to the strength of the evidence. For every inductive conclusion, assign a percentage of confidence, never using 100% as inductive conclusions are probabilistic, not certain. Use language that reflects this probability, such as "I'm 60% confident" instead of stating something definitively. You can also keep a confidence log for a week, recording predictions with probability levels and then checking if they were correct to train your calibration.

To "question the sample" when evaluating inductive claims, you should:

1. Ask about the number of observations: Inquire how many observations the claim is based on (e.g., "How many people were in the study?").
2. Learn rough sample sizes: Understand that different certainty levels require different sample sizes. For example, thousands of observations are needed for high confidence, and scientific certainty requires over 10,000.
3. Check the total number (denominator): Always look for the total number or population size from which the examples are drawn, as this is often hidden and reveals the weakness of a claim.
4. Track your own sample size: Before concluding a pattern, count how many times you've experienced something. For instance, three visits to a restaurant don't support an "always slow" conclusion; you need more data.
5. Challenge your sample: Compare your small sample with larger, potentially contradicting data sets (like online reviews) and trust the larger sample unless there's a specific reason to believe it's biased.