Having an Alpha of .05 and a Beta of .10 are the most common risk levels when running a Statistical test.

Question

Options

• ATrue
• BFalse

• A
  79% (19)
• B
  21% (5)

Explanation

Option A is correct because, by convention, researchers widely adopt an alpha (α) of .05 - meaning a 5% tolerance for a Type I error (false positive) - and a beta (β) of .10 - meaning a 10% tolerance for a Type II error (false negative) - as the standard thresholds in hypothesis testing. These values reflect an accepted balance between the risk of incorrectly rejecting a true null hypothesis versus failing to detect a real effect. Option B is incorrect because these are not arbitrary choices - they represent decades of consensus in scientific and statistical practice, making them genuinely the most common defaults.

Memory tip: Think "5 and 10" - alpha is the stricter guard (5%) because falsely claiming an effect exists is usually the more serious error, while beta gets double the tolerance (10%) since missing a real effect is considered slightly less costly in most research contexts. The relationship "beta = 2× alpha" is a handy anchor.

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