Contents
Why are p values uniformly distributed under h0?
The p-value is uniformly distributed when the null hypothesis is true and all other assumptions are met. The reason for this is really the definition of alpha as the probability of a type I error.
Does p-value prove null hypothesis?
A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.
Is the p-value the probability that h0 is true?
The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
What is the distribution of the test statistic under h0?
The sampling distribution of the test statistic under the null hypothesis is called the null distribution. When the data show strong evidence against the assumptions in the null hypothesis, the magnitude of the test statistic becomes too large or too small depending on the alternative hypothesis.
Why P-value is random variable?
The P-value is a random variable derived from the distribution of the test statistic used to analyze a data set and to test a null hypothesis. Under the null hypothesis, the P-value based on a continuous test statistic has a uniform distribution over the interval [0, 1], regardless of the sample size of the experiment.
How will you know if the null hypothesis should be rejected or accepted?
After you perform a hypothesis test, there are only two possible outcomes. When your p-value is less than or equal to your significance level, you reject the null hypothesis. When your p-value is greater than your significance level, you fail to reject the null hypothesis. Your results are not significant.