Reasoning with Observation and Uncertainty

• Inductive reasoning: premises strongly support a conclusion, but never absolute certainty
• Ampliative reasoning: inductive reasoning that 'amplifies' premises into a broader conclusion
• Ranking inductive arguments: determining the convincingness of arguments relative to each other
• Examples: ranking inductive arguments about a female US president
• SMART STUDY: applying induction in practical terms with critical thinking questions

Ranking Inductive Arguments

To apply induction in practical terms. Ranking inductive arguments helps us evaluate the strength of the reasoning behind a conclusion. It allows us to determine the likelihood of the conclusion being true based on the premises provided. By ranking these arguments, we can make informed decisions and apply induction in real-world scenarios.

Reasoning with Observation and Uncertainty

• Inductive reasoning: strongest with good evidence, weakest with little evidence or uncertainty
• General scenario: more likely than specific scenario
• Assessing inductive reasoning: how well does past knowledge guide future?
• Inductive strength: measure of likelihood of truth
• Cogent: good structure, conclusion not necessarily true
• Inductively forceful: good structure and true premises, conclusion likely true

Inductive Reasoning

Trivia: Inductive reasoning is the process of making generalizations based on specific observations or evidence. It is commonly used in scientific research and everyday life. However, it is important to note that inductive reasoning is not always reliable, as it can lead to incorrect conclusions when there is insufficient evidence or uncertainty.

• Inductive reasoning is often used to form hypotheses and theories.
• It involves moving from specific observations to broader generalizations.
• Inductive reasoning is based on probability rather than certainty.

Reasoning with Observation and Uncertainty

• Premise 1: Computer power and capabilities have been doubling around every two years for decades.
• Premise 2: [Implicit]
• Conclusion: Within two decades, the capabilities of computers will almost certainly have overtaken those of humans.

Computers Surpassing Humans

Trivia: According to the doubling of computer power and capabilities, computers are predicted to surpass human capabilities in the next two decades. This exponential growth in computing power opens up possibilities for advancements in artificial intelligence, machine learning, and data processing. With this rapid progress, the future of technology holds exciting potential for innovation and transformation. Get ready for a new era of computing!

Sampling Bias Examples

• Problem: Sampling from one spot on the beach.
• Problem: Sampling from only three locations.
• Problem: Including questionnaire with a political magazine.
• Problem: Using student volunteers from Harvard Business School.

Sampling Locations

Trivia: The passage mentions that a common problem is sampling from only three locations. This limited sampling can lead to biased results and may not accurately represent the entire population. It is important to have a diverse and representative sample to ensure reliable data.