A significance level of 0.01 is sometimes used instead of 0.05 in situations requiring greater confidence. Which scenario best illustrates this?

• A. When data are non-normally distributed
• B. When the p-value is exactly 0.05
• C. When human lives are involved
• D. When researchers want less stringent criteria

Answer: (C)

Lowering the significance level to 0.01 makes it harder to claim an effect exists because you need stronger evidence (a smaller p-value) before rejecting the null hypothesis. This reduces the chance of a false positive (concluding there is an effect when there isn’t one). In situations where human lives are involved, the cost of acting on a false positive is very high, so adopting a stricter criterion helps protect against dangerous decisions. That’s why the scenario where human lives are involved best illustrates using a more stringent 0.01 level.

The other options don’t fit as well: changing the data distribution doesn’t by itself require a stricter alpha; a p-value being exactly 0.05 is just a borderline case and doesn’t justify lowering the threshold; and wanting less stringent criteria would mean a higher alpha, not a lower one.