Reasoning and approaches of reasoning

 

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🧠 What is Reasoning?

Reasoning is the mental process of drawing conclusions from facts, observations, or assumptions.
In both animal science and economics, the way we reason determines how we:

• Analyze problems

• Test hypotheses

• Interpret data

• Make decisions

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There are three primary types of reasoning approaches:

1. Deductive reasoning

2. Inductive reasoning

3. Abductive reasoning

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1️⃣ Deductive Reasoning: From General to Specific

🔍 Definition:

Deductive reasoning starts with a general principle or theory and applies it to a specific case to reach a logically certain conclusion.

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✔️ Structure:

If A is true, and B fits A, then B must be true.

🐄 Animal Science Example:

• Premise 1: All ruminants have a four-chambered stomach.

• Premise 2: Cows are ruminants.

• ➡️ Conclusion: Cows have a four-chambered stomach.

💹 Economics Example:

• Premise 1: When demand increases and supply remains constant, prices rise.

• Premise 2: Demand for wheat has increased.

• ➡️ Conclusion: Wheat prices will rise (assuming supply is unchanged).

🎯 Use:

Deductive reasoning is common in:

• Scientific theories

• Policy frameworks

• Lab protocols and standards

• Economic modeling

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2️⃣ Inductive Reasoning: From Specific to General

🔍 Definition:

Inductive reasoning involves drawing general conclusions based on specific observations. The conclusion is probable, not guaranteed.

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✔️ Structure:

Observe → Identify a pattern → Form a general rule

🐔 Animal Science Example:

• Observation 1: Hens on farm A produce more eggs with organic feed.

• Observation 2: Hens on farm B also show higher egg production with organic feed.

• ➡️ Conclusion: Organic feed may increase egg production.

📈 Economics Example:

• Observation 1: Lower interest rates led to increased investment in country X.

• Observation 2: The same happened in country Y.

• ➡️ Conclusion: Lower interest rates probably stimulate investment.

🎯 Use:

Inductive reasoning is used in:

• Data analysis

• Field observations

• Market research

• Hypothesis formation

• Policy evaluation

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3️⃣ Abductive Reasoning: Best Explanation

🔍 Definition:

Abductive reasoning starts with an incomplete set of observations and tries to find the most likely explanation.

It’s the logic we often use in diagnosing problems.

✔️ Structure:

You observe something surprising → Ask: “What’s the most likely cause?”

🐕 Animal Science Example:

• Observation: A group of animals shows signs of dehydration, but water is available.

• ➡️ Hypothesis: The water source might be contaminated or unpalatable.
  (You didn’t observe the cause directly, but infer the most likely explanation.)

🏦 Economics Example:

• Observation: A sharp decline in consumer spending in one region.

• ➡️ Possible Explanation: Maybe a sudden policy change, unemployment, or inflation shock.

🎯 Use:

Abductive reasoning is commonly used in:

• Veterinary diagnosis

• Behavioural analysis

• Economic forecasting

• Crisis response

• Detecting unseen causes from observed effects

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🧭 Comparison Table

Feature	Deductive Reasoning	Inductive Reasoning	Abductive Reasoning
Direction	General → Specific	Specific → General	Observation → Most likely explanation
Conclusion certainty	Always true if premises are true	Probably true	Possibly true
Common use in science	Theoretical models	Data collection & hypothesis	Diagnosing & explaining anomalies
Common use in economics	Policy logic and assumptions	Empirical studies	Interpreting market shocks

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🧪 Why This Matters in Animal Science & Economics

Animal Science	Economics
Testing biological theories (deductive)	Applying economic models (deductive)
Field studies on livestock (inductive)	Trend analysis and forecasting (inductive)
Diagnosing animal diseases (abductive)	Explaining inflation or poverty spikes (abductive)

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✅ Summary

• Deductive: Moves from general rules to specific conclusions – good for testing established theories.

• Inductive: Builds general ideas from specific data – great for discovery and research.

• Abductive: Provides the best explanation when not all facts are known – essential for problem-solving.

💡 No one method is “better” — they are complementary. Real-world decision-making often combines all three.