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.
To arrive at this conclusion, the statistician compared the obtained P-value (1.8%) with the chosen cutoff for statistical significance (4%). Since the obtained P-value is lower than the significance level, the statistician rejects the null hypothesis (the treatment does nothing) in favor of the alternative hypothesis (the treatment does something).
2. The quoted statement is false.
The P-value (1.8%) does not directly measure the probability that the treatment does or does not do anything. The P-value represents the probability of obtaining results as extreme as, or more extreme than, the observed data, assuming that the null hypothesis is true. It is not a measure of the likelihood of the null hypothesis being true or false.