When your p-value is less than or equal to your significance level, you reject the null hypothesis. Your results are statistically significant. When your p-value is greater than your significance level, you fail to reject the null hypothesis. Your results are not significant.

It is by no means a failure if your data do not support your hypothesis; in fact, that can be more interesting than the other way around, because you may find a new perspective for looking at the data. Failure to support hypotheses is common in science, and often serves as a starting point for new experiments.

A hypothesis is an educated guess or prediction about the relationship between two variables. The objective of a hypothesis is for an idea to be tested, not proven. The results of a hypothesis test can demonstrate only whether that specific hypothesis is or is not supported by the evidence.

A hypothesis is a proposed idea that may explain an observation or phenomena. It is verified by testing it. If the data supports the hypothesis, then we consider the hypothesis to be verified and true.

After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)

When we fail to reject the null hypothesis when the null hypothesis is false. The “reality”, or truth, about the null hypothesis is unknown and therefore we do not know if we have made the correct decision or if we committed an error.

If the P-value is less than (or equal to) , then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than , then the null hypothesis is not rejected. If the P-value is greater than , do not reject the null hypothesis.

Understanding a Type II Error A type II error does not reject the null hypothesis, even though the alternative hypothesis is the true state of nature. In other words, a false finding is accepted as true.

14 Answers. Failing to reject a null hypothesis is evidence that the null hypothesis is true, but it might not be particularly good evidence, and it certainly doesn’t prove the null hypothesis.

Technically, no, a null hypothesis cannot be proven. For any fixed, finite sample size, there will always be some small but nonzero effect size for which your statistical test has virtually no power.

Sample evidence can prove that a null hypothesis is true. The correct answer is False because although sample data is used to test the null​ hypothesis, it cannot be stated with​ 100% certainty that the null hypothesis is true.

A type I error, or false positive, is asserting something as true when it is actually false. This false positive error is basically a “false alarm” – a result that indicates a given condition has been fulfilled when it actually has not been fulfilled (i.e., erroneously a positive result has been assumed).

In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a “false positive” finding or conclusion; example: “an innocent person is convicted”), while a type II error is the non-rejection of a false null hypothesis (also known as a “false negative” finding or conclusion …