1. What is a Hypothesis?

A hypothesis is a testable statement that predicts a relationship between variables.
It is usually based on prior research, clinical observations, or theory.
In research, hypotheses guide study design, data collection, and statistical testing.
A strong hypothesis is clear, measurable, and logically justified.

2. Null vs Alternative Hypothesis

The Null Hypothesis (H₀) states that there is no difference or no association between groups.
The Alternative Hypothesis (H₁ or Ha) states that there is a difference or association.
Statistical tests are designed to determine whether we reject or fail to reject H₀.
Most research aims to find evidence supporting the alternative hypothesis.

3. Type I Error (False Positive)

A Type I error happens when the researcher rejects the null hypothesis even though it is true.
This means concluding there is a significant result when there actually isn’t.
The probability of Type I error is represented by α (alpha), commonly set at 0.05.
It is also called a false positive result.

4. Type II Error (False Negative)

A Type II error happens when the researcher fails to reject the null hypothesis even though it is false.
This means missing a real effect or association that actually exists.
The probability of Type II error is represented by β (beta).
It is also called a false negative result, often reduced by increasing sample size.

Examples (For Better Understanding)

✅ Example 1 (Type I Error):
A study claims a new drug lowers blood pressure, but in reality it does not.
→ Researcher wrongly rejects H₀ = False Positive (Type I error)

✅ Example 2 (Type II Error):
A study concludes a drug has no effect on cholesterol, but it actually works.
→ Researcher fails to reject H₀ = False Negative (Type II error)

✅ Example 3 (Hypothesis Example):
H₀: Smoking has no association with heart disease.
H₁: Smoking increases the risk of heart disease.