The probability of accepting the null hypothesis (Ho) when the alternative (H1) is true.

Question

Options

• AElementary Outcomes
• BBeta Risk
• CAttribute Data
• DAlpha Risk

• A
  5% (1)
• B
  90% (19)
• C
  5% (1)

Explanation

Beta Risk (Type II Error) is the probability of failing to reject a false null hypothesis - in other words, accepting H₀ when H₁ is actually true. This is the definition of a "missed detection" in hypothesis testing, and it is directly controlled by the value β (beta).

Why the distractors are wrong:

• Alpha Risk (D) is the opposite error - rejecting H₀ when it is actually true (Type I Error, false positive), controlled by α.
• Elementary Outcomes (A) refers to the individual possible results of a probability experiment (e.g., rolling a die), unrelated to hypothesis testing errors.
• Attribute Data (C) is a data classification term referring to pass/fail or categorical data, not an error type in statistical testing.

Memory tip: Think "Beta = Blind miss" - you miss (fail to catch) the truth when H₁ is real. Alpha = "Alarm goes off when it shouldn't" (false alarm). Alpha and Beta are opposites: one fires when it shouldn't, the other stays silent when it should fire.

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