A hypothesis is the first step in running a statistical test (t-test, chi-square test, etc.) A NULL HYPOTHESIS is the probability that what you are testing does NOT occur. An ALTERNATIVE HYPOTHESIS is the probability that what you are testing DOES occur.
Failing to reject a false null hypothesis.
I think you are asking: What is hypothesis testing in the field of statistics. See: http://en.wikipedia.org/wiki/Statistical_hypothesis_testing
A non-directional research hypothesis is a kind of hypothesis that is used in testing statistical significance. It states that there is no difference between variables.
No. The null hypothesis is assumed to be correct unless there is sufficient evidence from the sample and the given criteria (significance level) to reject it.
forming a hypothesis is when you come up with an educated guess.. what you think it may be . testing a hypothesis is when you're testing to see if someone else's guess is right.
Concluding that the hypothesis is correct based on personal beliefs or opinions is not part of testing a hypothesis. Testing a hypothesis involves designing experiments, collecting data, and analyzing results to determine if the hypothesis is supported or not.
examining/ experimenting/ testing/ verifying... it depends on the type of hypothesis to an extent I think.
A hypothesis is a suggestion of a way to explain something. If the hypothesis is tested and confirmed, it can advance to the status of theory. The conclusion of testing a hypothesis will be either that the hypothesis is confirmed, or it is not confirmed.
The purpose of controlling the environment when testing a hypothesis is ultimately to get a reliable result to the study.
Rejecting a true null hypothesis.
You use a z test when you are testing a hypothesis that is using proportions You use a t test when you are testing a hypothesis that is using means
an experiment
By testing.
A hypothesis is a proposed explanation which scientists test with the available scientific theories. There are four steps to testing a hypothesis; state the hypothesis, formulate an analysis plan, analyze sample data and interpret the results.
the process is to know what they hypothesis means
The probability of correctly detecting a false null hypothesis.