What is the difference between Type I and Type II error?

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In hypothesis testing, Type I and Type II errors are two types of incorrect conclusions a researcher can make when evaluating evidence against a null hypothesis (H₀).

✅ Type I Error (False Positive)

• Definition: Rejecting the null hypothesis when it is actually true.

• You say there's an effect or difference, but there isn't.

• Symbol: α (alpha), typically set at 0.05 (5%).

• Example: You conclude a new drug works, but it actually doesn’t.

🧠 Think: “Crying wolf” — you detect something that isn’t there.

✅ Type II Error (False Negative)

• Definition: Failing to reject the null hypothesis when it is actually false.

• You say there’s no effect or difference, but there is.

• Symbol: β (beta)

• Power of a test = 1 − β (the ability to detect a true effect)

• Example: You conclude a new drug doesn’t work, but it actually does.

🧠 Think: “Missing the signal” — you fail to detect something that is real.

🎯 Summary:

• Type I Error: Believing there's an effect when there isn’t.

• Type II Error: Missing an effect that actually exists.

A well-designed study aims to minimize both errors, but there's often a trade-off between them.

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