The hypothesis tests compare weather an event is meant to alter a population mean results, for example, a scientist experiment might have or not have a significant effect over the population results. The test aims to reject the null hypothesis, so what it really want to find out is if the alternative Hypothesis H1 is likely true. The null hypothesis is the probability that the results are not due to chance – if it’s rejected, then the results are due to chance.The level of significance , or so called p-value, is the probability that the null hypothesis (H0) happen , If p is very small then the null hypothesis is rejected - isn’t true- and the alternative Hypothesis is accepted. A higher P value implies a higher probability than results are not happening so that the H0 is accepted and H1 rejected. The null Hypothesis will normally will rejected when the level of significance are either lower than 0.05 or 0.01, the lower P value the higher the level of confidence that the results are due to chance.
Since the first part of the statement (A p is the probability that the results are not due to chance) is correct, and the second part is wrong (…the probability that the null hypothesis (H0) is false), the total statement is false. The correct statement would be as follows : A p is the probability that the results are not due to chance, the probability that the null hypothesis (H0) is true.
