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Statistical / Factual Cause–Effect

Introduction

Statistical या Factual Cause-Effect प्रश्न data trends, numerical patterns, या real-world statistics पर आधारित होते हैं। ऐसे प्रश्नों में दोनों statements अक्सर measurable facts बताते हैं, और आपको पहचानना होता है कि क्या एक fact दूसरे को cause कर रहा है या दोनों सिर्फ correlated हैं। यह पैटर्न assumptions के बजाय observed data पर आधारित logical reasoning विकसित करने में मदद करता है।

Pattern: Statistical / Factual Cause–Effect

Pattern

मुख्य अवधारणा: जब दोनों statements factual या numerical trends दिखाएँ, तो cause वही होगा जो दूसरे trend को logically समझाता या उत्पन्न करता है।

Step-by-Step Example

Question

1️⃣ Online learning apps का उपयोग बढ़ गया है।
2️⃣ Internet data consumption तेज़ी से बढ़ गया है।

सही cause-effect संबंध कौन सा है?
(A) 1 → Cause; 2 → Effect
(B) 2 → Cause; 1 → Effect
(C) दोनों एक common cause के effects हैं
(D) दोनों independent हैं

Solution

  1. Step 1: Statistical facts पहचानें

    दोनों statements measurable बढ़ोतरी दिखाते हैं - एक app usage में, दूसरा data consumption में।

  2. Step 2: Logical दिशा तय करें

    Apps का अधिक उपयोग सीधे internet data usage बढ़ाता है।

  3. Step 3: Reversal जाँचें

    सिर्फ data usage बढ़ने से app usage नहीं बढ़ता - इसका उल्टा अधिक तर्कसंगत है।

  4. Final Answer:

    1 → Cause; 2 → Effect → Option A

  5. Quick Check:

    यदि app usage बंद हो जाए, तो data consumption भी घट जाएगा - cause पुष्टि हुआ ✅

Quick Variations

1. Data surveys, sales reports, या market trends से मिल सकता है।

2. आमतौर पर एक trend दूसरा trend drive करता है - जैसे income vs expenditure, price vs demand, usage vs cost।

3. कभी-कभी दोनों facts किसी बाहरी कारण से भी उत्पन्न हो सकते हैं (जैसे festival season, policy change)।

Trick to Always Use

  • Directional dependency खोजें - कौन-सी घटना logically दूसरी को बढ़ाती या घटाती है।
  • देखें दोनों data points सिर्फ साथ-साथ बदल रहे हैं (correlation) या एक दूसरे को drive कर रहे हैं (causation)।
  • अगर doubt हो, एक factor को हटाकर सोचें - अगर दूसरा बदलता है, तो पहला likely cause है।

Summary

Summary

  • Statistical या Factual Cause-Effect प्रश्न quantitative trends या factual changes पर आधारित होते हैं।
  • Cause वह होता है जो दूसरे variable में measurable change लाता है।
  • Correlation ≠ causation - दोनों facts के बीच logical link होना ज़रूरी है।
  • Exam में ऐसे प्रश्न data reasoning और practical interpretation skills को test करते हैं।

याद रखने के लिए उदाहरण:
“App usage increased → Internet data usage rose.”

Practice

(1/5)
1. 1️⃣ The sales of air conditioners have risen sharply. 2️⃣ The average temperature recorded this summer is higher than usual. Identify the correct cause-effect relationship.
easy
A. 2 → Cause; 1 → Effect
B. 1 → Cause; 2 → Effect
C. Both are effects of a common cause
D. Both are independent

Solution

  1. Step 1: Examine the facts

    Both statements present measurable data - sales figures and temperature levels.

  2. Step 2: Identify logical direction

    High temperature leads to more AC sales, not vice versa.

  3. Step 3: Confirm reasoning

    Weather (temperature) influences consumer purchases.

  4. Final Answer:

    2 → Cause; 1 → Effect → Option A

  5. Quick Check:

    If temperatures were low, AC sales wouldn’t rise ✅
Hint: Environmental data (like temperature) usually acts as the cause for consumer trends.
Common Mistakes: Assuming product sales affect temperature.
2. 1️⃣ The number of road accidents has decreased. 2️⃣ The enforcement of traffic rules has become stricter. Identify the correct cause-effect sequence.
easy
A. 1 → Cause; 2 → Effect
B. Both are effects of a common cause
C. 2 → Cause; 1 → Effect
D. Both are independent

Solution

  1. Step 1: Identify factual connection

    Both are measurable outcomes - accident rate and rule enforcement.

  2. Step 2: Logical dependency

    Stricter rule enforcement (cause) → fewer accidents (effect).

  3. Step 3: Reverse check

    Fewer accidents don’t cause rules to become strict.

  4. Final Answer:

    2 → Cause; 1 → Effect → Option C

  5. Quick Check:

    If traffic laws were lenient, accidents would increase ✅
Hint: Policy enforcement data generally acts as the cause for safety statistics.
Common Mistakes: Mixing up policy outcome with implementation.
3. 1️⃣ Mobile data usage has increased by 40%. 2️⃣ The number of video streaming users has grown rapidly. Determine the correct cause-effect relationship.
easy
A. 1 → Cause; 2 → Effect
B. 2 → Cause; 1 → Effect
C. Both are effects of a common cause
D. Both are independent

Solution

  1. Step 1: Observe correlation

    Both are factual usage trends - data and streaming users.

  2. Step 2: Logical link

    More streaming → more data consumption.

  3. Step 3: Verify reverse

    Higher data usage doesn’t directly cause streaming increase.

  4. Final Answer:

    2 → Cause; 1 → Effect → Option B

  5. Quick Check:

    Less streaming → lower data usage ✅
Hint: Usage behaviour often triggers data changes, not the reverse.
Common Mistakes: Confusing correlation with causation between two rising statistics.
4. 1️⃣ The sales of electric vehicles (EVs) increased this year. 2️⃣ The government reduced taxes on EVs. Identify the correct cause-effect pattern.
medium
A. 1 → Cause; 2 → Effect
B. 2 → Cause; 1 → Effect
C. Both are independent
D. Both are effects of a common cause

Solution

  1. Step 1: Examine facts

    Government tax policy and EV sales figures are measurable trends.

  2. Step 2: Logical link

    Tax reduction (cause) → sales growth (effect).

  3. Step 3: Verify

    Sales rise doesn’t lead to tax reduction directly.

  4. Final Answer:

    2 → Cause; 1 → Effect → Option B

  5. Quick Check:

    No tax cuts → sales remain lower ✅
Hint: Economic policy data almost always precedes market trend data.
Common Mistakes: Assuming popularity forced tax cuts instead of the reverse.
5. 1️⃣ The number of hospital admissions for respiratory issues increased. 2️⃣ The city’s air pollution level rose significantly. Identify the correct cause-effect order.
medium
A. 1 → Cause; 2 → Effect
B. Both are effects of a common cause
C. 2 → Cause; 1 → Effect
D. Both are independent

Solution

  1. Step 1: Identify measurable data

    Hospital admissions and pollution levels are factual metrics.

  2. Step 2: Logical connection

    Rising pollution (cause) → higher respiratory illness (effect).

  3. Step 3: Cross-check

    Admissions don’t cause pollution; the reverse is true.

  4. Final Answer:

    2 → Cause; 1 → Effect → Option C

  5. Quick Check:

    Cleaner air → fewer hospital visits ✅
Hint: Environmental data usually drives health statistics - not the other way around.
Common Mistakes: Reversing cause and effect order based on occurrence timing.

Mock Test

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