A research article reports the results of a new drug test. The null hypothesis is that the drug has no effect. The alternative hypothesis is that the drug decreases vision loss in people with Macular Degeneration (i.e., the drug is effective). The article gives a p-value of 0.04 in the analysis section. Indicate which of the following interpretations of the p-value are correct. NOTE: There could be more than one answer. A. The probability of getting results at least as extreme as the ones in the study if the drug is actually not effective. B. The probability that the drug is effective. C. The probability that the drug is not effective.

The p-value is defined as the probability corresponding to the calculated statistic if possible under the null hypothesis (i.e. the probability of obtaining a value as extreme as the value of the statistic under the null hypothesis).

In the example, the results of a drug test were reported, being the null hypothesis "the drug has no effect" and the alternative hypothesis "the drug decreases vision loss in people with Macular Degeneration"

After conducting the test, the researchers obtained the p-value 0.004

Taking the previous definition of the p-value, the correct answer is A.

To tell whether B. and C. are correct or incorrect there should be specified what signification level was used in the analysis. Remember, the p-value is the probability of the statistic value under the null hypothesis and to use it to make a decision ver the null hypothesis you have to compare it with the signification level. If the p-value is greater than the level of significance, then you don't reject the null hypothesis (Then you can conclude that the drug has no effect) If the p-value is equal or less than the level of significance, then you reject the null hypothesis. (Then you can conclude that the drug decreases the vision loss)

Using a level of signification of 0.01 then the decision is to not reject the null hypothesis but with levels of 0.05 or 0.1 then the decision is to reject it. This is why it is important to know what level of significance was used in the test when interpreting the p-value.

I hope it helps!