🔬 1. Basic Research Structure (Simple Flow)

Step-by-step:

1. Research Question
   → What are you trying to find out?
2. Hypothesis (Prediction)
   → A clear, testable statement (If… then…)
3. Null Hypothesis (H₀)
   → Says no effect or no difference
4. Alternative Hypothesis (Hₐ)
   → Says there is an effect or difference
5. Method (Research/Study Type)
   → Experiment or observational study
6. Data Collection (Measurements)
   → Weight, number, time, etc.
7. Analysis (Test Statistic)
   → Use sample data to calculate results
8. Conclusion
   → Reject or fail to reject H₀

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🧠 2. Writing a Good Hypothesis

Formula:

If (change), then (effect), because (reason).

Rules:

• Be clear and specific
• Avoid weak words like “could” or “should”
• Include:
  • Cause (independent variable)
  • Effect (dependent variable)

Example:

• ✅ If plants receive 5g more water per day, then their growth will increase because water supports cell expansion.

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⚖️ 3. Hypotheses Types

Null Hypothesis (H₀)

• No difference / no effect
• Example:
  → H₀: Water amount has no effect on plant growth.

Alternative Hypothesis (Hₐ)

• There is a difference/effect
• Example:
  → Hₐ: Increased water leads to greater plant growth.

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📊 4. Variables

Type	Meaning	Example
Independent	What you change	Water amount
Dependent	What you measure	Plant growth
Control	What stays the same	Light, soil

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👥 5. Samples & Data

• Population → Whole group (e.g., all people in the UK)
• Sample → Smaller group studied (e.g., 50 people)
• Random Sample → Everyone has an equal chance

Example:

• Sample size: 50 plants
• Measure: height in cm

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🔍 6. Research Types

Experimental Study

• You change something
• Example: change water levels

Observational Study

• You watch only
• Example: record how many people have mental health conditions

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📏 7. Measurements

Examples:

• Weight (grams, kg)
• Height (cm)
• Time (minutes)
• Counts (number of people)

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📈 8. Test Statistic & Significance

Test Statistic

• A number calculated from sample data
• Helps decide if results are real or random

Significance Level (α)

• Usually 0.05 (5%)
• Means:
  → Only 5% chance results are random

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✅ 9. Decision Outcomes

Result	Meaning
Reject H₀	There IS an effect
Fail to Reject H₀	NOT enough evidence

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🌱 10. Full Example

Research Topic:

Effect of water on plant growth

Question:

→ Does more water increase plant growth?

Hypotheses:

• H₀: Water amount does not affect growth
• Hₐ: More water increases growth

Study:

• 50 plants (sample)
• Randomly split into groups
• Measure height

Data:

• Calculate average (mean)
• Use test statistic

Result:

• If statistically significant → Reject H₀
• If not → Fail to reject H₀

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🧩 11. Your Formula Summary (Clean Version)

• Question → What is happening?
• Hypothesis → If… then… because…
• Identify → Cause & effect
• Use → Random sample
• Measure → Clearly (numbers)
• Analyze → Test statistic
• Decide → Reject or not reject H₀