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Type II error occurs when:
Question

Type II error occurs when:

A.

H₀ (Null hypothesis) is true and is accepted

B.

H₀ (Null hypothesis) is true and is rejected

C.

H₀ (Null hypothesis) is false and is accepted

D.

Hₐ (Alternate hypothesis) is false and is rejected

Correct option is C

Introduction:

A Type II error occurs when the null hypothesis (H₀) is false but is accepted (i.e., not rejected). In simple terms, we fail to detect an effect or difference when one actually exists. This is also called a false negative.

In hypothesis testing, when we make a decision about the null hypothesis, there are four possible outcomes:

  • If H₀ is true and we accept it → Correct decision

  • If H₀ is true and we reject it → Type I error (false positive)

  • If H₀ is false and we reject it → Correct decision

  • If H₀ is false and we accept it → Type II error (false negative)

So, when we accept a false null hypothesis, we commit a Type II error.

Information Booster:

  • Type II error (β) means we fail to reject a false null hypothesis.

  • It leads to missing a real effect, such as failing to detect the success of a treatment.

  • The probability of making a Type II error is denoted by β, and the power of a test is defined as 1 − β.

  • A powerful test has a low probability of Type II error.

  • Type II error can be minimized by increasing the sample size or using more sensitive testing methods.

  • In fields like medicine or manufacturing, Type II errors can lead to risky decisions due to missed detections.

  • Statisticians carefully design studies to balance both Type I and Type II error risks.

    Additional Information :

  • Option (a): H₀ is true and is accepted .This is a correct decision, not an error. If the null hypothesis is actually true and we accept it, there’s no mistake.

  • Option (b): H₀ is true and is rejected
    This is a Type I error. Here, we reject a true null hypothesis — a false positive — thinking an effect exists when it doesn’t.

  • Option (d): Hₐ is false and is rejected
    This is again a correct decision. If the alternative hypothesis is false and we reject it, then our conclusion is accurate.

    True Situation
    Decision: Accept H₀
    Decision: Reject H₀
    H₀ True
    Correct (1-α)
    Type I Error (α)
    H₀ False
    Type II Error (β)
    Correct (Power = 1-β)

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