Null Hypotheses and Alternative Statistical Hypotheses
There are three types of hypotheses:
- Research hypotheses
- Statistical hypotheses
- Substantive hypotheses
We will focus on testing statistical hypotheses
All statistical hypotheses, containing all possible outcomes of the experiment or study, consist of two parts: null hypothesis and alternative hypothesis.
Null Hypotheses
The hypothesis that an apparent effect is due to chance is called the null hypothesis. The null hypothesis states that the “null” condition exists; that is, there is nothing new happening, the old theory is still true, the old standard is correct, and the system is in control. The null hypothesis is typically the opposite of the researcher's hypothesis
The maxim, innocent until proven guilty helped my understanding of null hypothesis. If laid out as a hypotheses test, “innocent until proven guilty” would be the null hypotheses.
A null hypothesis is a statistical hypothesis that is tested for possible rejection under the assumption that it is true.
The null hypothesis, H0, represents a theory that has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved. We give special consideration to the null hypothesis. This is due to the fact that the null hypothesis relates to the statement being tested, whereas the alternative hypothesis relates to the statement to be accepted if and when the null is rejected.
The final conclusion once the test has been carried out is always given in terms of the null hypothesis. We either Reject H0 in favor of Ha or We fail to reject H0; we never conclude "Reject Ha", or even "Accept Ha".
If we conclude "Do not reject H0", this does not necessarily mean that the null hypothesis is true; it only suggests that there is not sufficient evidence against H0 in favor of Ha. Rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.[1]
It is possible for an experiment to fail to reject the null hypothesis; in this case, the null hypothesis is considered sufficient to explain the data and no alternative hypothesis needs to be devised or tested.[2]
