NULL HYPOTHESIS

by Martyn Shuttleworth (2008)

The null hypothesis, H0, is an essential part of any research design, and is always tested, even indirectly.

The simplistic definition of the null is as the opposite of the alternative hypothesis, H1, although the principle is a little more complex than that.

The null hypothesis is a hypothesis which the researcher tries to disprove, reject or nullify.

The 'null' often refers to the common view of something, while the alternative hypothesis is what the researcher really think is the cause of a phenomenon.

An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.

Despite this, many researchers neglect the null hypothesis when testing hypotheses, which is poor practice and can have adverse effects.

EXAMPLES OF THE NULL HYPOTHESIS

A researcher may postulate a hypothesis:

H1: Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil.

And a null hypothesis:

H0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than soil.

It is important to carefully select the wording of the null, and ensure that it is as specific as possible. For example, the researcher might postulate a null hypothesis:

H0: Tomato plants show no difference in growth rates when planted in compost rather than soil.

There is a major flaw with this null hypothesis. If the plants actually grow more slowly in compost than in soil, an impasse is reached. H1 is not supported, but neither is H0, because there is a difference in growth rates.

If the null is rejected, with no alternative, the experiment may be invalid. This is the reason why science uses a battery of deductive and inductive processes to ensure that there are no flaws in the hypotheses.

Many scientists neglect the null, assuming that it is merely the opposite of the alternative, but it is good practice to spend a little time creating a sound hypothesis. It is not possible to change any hypothesis retrospectively, including H0.

SIGNIFICANCE TESTS

If significance tests generate 95% or 99% likelihood that the results do not fit the null hypothesis, then it is rejected, in favor of the alternative.

Otherwise, the null is accepted. These are the only correct assumptions, and it is incorrect to reject, or accept, H1.

Accepting the null hypothesis does not mean that it is true. It is still a hypothesis, and must conform to the principle of falsifiability, in the same way that rejecting the null does not prove the alternative.

PERCEIVED PROBLEMS WITH THE NULL

The major problem with the null hypothesis is that many researchers, and reviewers, see accepting the null as a failure of the experiment. This is very poor science, as accepting or rejecting any hypothesis is a positive result.

Even if the null is not refuted, the world of science has learned something new. Strictly speaking, the term ‘failure’, should only apply to errors in the experimental design, or incorrect initial assumptions.

DEVELOPMENT OF THE NULL

Up until the 1500's most people thought that the world was flat (At the time: The null hypothesis). Columbus challenged this idea with the alternative hypothesis: The world is round. Then most people thought that the earth was the center of the universe (The 'new' null hypothesis).

Copernicus had an alternative research hypothesis that the world actually circled around the sun, thus being the center of the universe. Eventually, people got convinced and accepted it as the null.

Later someone proposed an alternative hypothesis that the sun itself also circled around the something within the galaxy. This is how research works - the null hypothesis get's closer to the reality each time, even if it isn't correct, it is better than the last null hypothesis.

Source: http://www.experiment-resources.com/null-hypothesis.html

Citation:

Shuttleworth, Martyn (2008). Null Hypothesis. Retrieved [Date of Retrieval] from Experiment Resources: http://www.experiment-resources.com/null-hypothesis.html