The p-value is the probability of the observed statistic given the null hypothesis is true.
A) True
B) False
False?
P value (e.g., P ≤ .05) is the probability that the null hypothesis is true, even though it may be rejected at this level.
That is correct. Option B) False is the correct answer.
The p-value is actually the probability of obtaining a test statistic as extreme as, or more extreme than, the observed value, assuming that the null hypothesis is true. It quantifies the strength of evidence against the null hypothesis.
To determine the p-value, you would first calculate the test statistic (such as t-value, z-value, or F-statistic) using the observed data. Then, you compare this test statistic to the appropriate distribution (such as t-distribution, standard normal distribution, or F-distribution) under the assumption that the null hypothesis is true. Based on the distribution, you find the probability of obtaining a test statistic as extreme as or more extreme than the observed value. This probability is the p-value.
If the p-value is small (typically less than a pre-determined threshold, such as 0.05), it suggests strong evidence against the null hypothesis and supports the alternative hypothesis. If the p-value is large, it suggests weak evidence against the null hypothesis and fails to support the alternative hypothesis.