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A hypothesis comes before data. A hypothesis is an estimated guess to what will happen. And Data is the steps it takes to come to a solution in a problem.
In the scientific method this would be called the "hypothesis".
Statistical inference is about testing hypotheses. In order to test a hypothesis, you make a prediction about the observations, contrasting the prediction with what might happen if the hypothesis were not true. The prediction is tested against the observations by calculating a test statistic or inferential statistic. This is a value which is based purely on the observations. If the test statistic is too far from the predicted value then the hypothesis should be rejected in favour of the alternative hypothesis.What constitutes "too far" depends on the presumed distribution of the variable being tested, as well as the degree of certainty required from the test - the power of the test. The latter is a balance between probability of rejecting the hypothesis when it is true and that of not rejecting it when it is false. These outcomes may be weighted according to the risk or costs that a false decision carries.
It means something nearly always happens. It is used to indicate that something is almost guaranteed to happen.
There is insufficient information in the question to properly answer it. You did not provide the list of "the following". Please restate the question. However, by definition of probability, a probability less than 0 (the event will never happen) or greater than 1 (the event will always happen) is impossible, so maybe that answers your question.