A statistician analyzing a randomized controlled experiment has tested
Null: The treatment does nothing.
Alternative: The treatment does something.
using a 4% cutoff for P-values. The P-value of the test turns out to be about 1.8%.
1.
The conclusion of the test is
-The treatment does nothing.
-The treatment does something.
2.
“P=1.8% means that there is only about a 1.8% chance that the treatment does nothing.”
The quoted statement is
- True
- False
1. The conclusion of the test is that the treatment does something. This is because the P-value is less than the predetermined significance level (4%), which means that the observed data provides strong evidence against the null hypothesis (that the treatment does nothing) and supports the alternative hypothesis (that the treatment does something).
2. The quoted statement is false. The correct interpretation of the P-value is that if the null hypothesis (that the treatment does nothing) were true, there would be a 1.8% chance of observing data as extreme as what was actually observed. It does not measure the probability that the treatment does nothing. The P-value is used to make a decision about the null hypothesis, not to assess the probability of the null hypothesis being true.