The finding is significant at the ?0.01? level Hopefully by now it’s not too surprising by now that all of these are equivalent statements: The less likely it is that we obtained a result by chance, the more significant our results. The significance (or statistical significance) of a test is the probability of obtaining your result by chance. If, however, we’d picked a more rigorous ?\alpha=0.05? or ?\alpha=0.01?, we would have failed to reject the null hypothesis every time. So we would have rejected the null hypothesis for both one-tailed tests, but we would have failed to reject the null in the two-tailed test. With these in mind, let’s say for instance you set the confidence level of your hypothesis test at ?90\%?, which is the same as setting the ?\alpha? level at ?\alpha=0.10?. ?p=0.0721? for the upper-tail one-tailed test ?p=0.0721? for the lower-tail one-tailed test If ?p>\alpha?, do not reject the null hypothesis If ?p\leq \alpha?, reject the null hypothesis Whether or not you should reject ?H_0? can be determined by the relationship between the ?\alpha? level and the ?p?-value. The reason we’ve gone through all this work to understand the ?p?-value is because using a ?p?-value is a really quick way to decide whether or not to reject the null hypothesis.
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