Respuesta :
The definition of the p-value is the probability that we would see a test statistic this extreme or more if the null hypothesis were true.
The first option is correct.
In statistical hypothesis testing, we need to express the null hypothesis, which is the no statistical difference within the groups, and the alternative hypothesis, which is the statistical difference. Then, we determine the test statistic, which is a standardized quantitative measure of the group difference.
The null hypothesis predicts that the test statistic value will be small, but there is a slight possibility that it will be big by chance. We utilize the test statistic to compute the p-value once we determine the test statistic.
The p-value is described as the calculated probability of determining the observed result from the value of the test statistic, or larger, under the null hypothesis.
The extreme now depends on the extent to which the hypothesis is being tested.
Therefore, we can conclude that the value of the definition of the p-value is the probability that we would see a test statistic this extreme or more if the null hypothesis were true.
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The definition of the p-value is A. The probability that we would see a test statistic this extreme or more if the null hypothesis were true.
- It should be noted that the null hypothesis simply means that there's no statistical difference within the groups while the alternative hypothesis is the statistical difference.
- The null hypothesis predicts that the test statistic value will be small. We use the test statistic to compute the p-value once we determine the test statistic.
- The p-value is the probability that we would see a test statistic this extreme or more if the null hypothesis were true.
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