Testing hypothesis is mostly done by comparing an experiment idea with a null by hypothesis. In testing hypothesis, careful construction of two statements is done: the null hypothesis and the alternative hypothesis.
The null hypothesis which is formulated mathematically as H o reflects that there is no observed effect in an experiment. In an experiment the researcher will be trying to find evidence against the null hypothesis in order to have an effective hypothesis testing. A null hypothesis might be rejected when the P value is smaller or lower than the level of significance Alpha (Lock et al.,l 2012). When the P-value is greater than Alpha the researcher will fail to reject the null hypothesis. If the null hypothesis is not rejected the researcher need to provide adequate explanation. This is because if the researcher fails to reject a null hypothesis it does not guarantee that the statement is true. An example of null hypothesis is a claim that police officers serving in low income areas do not experience the same blood pressure rate with those of the general police officer population.
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An alternative hypothesis is the inverse or opposite of the null hypothesis. The alternative hypothesis shows that there is an observed effect in the experiment. Unlike the null hypothesis which has an equal sign in its formulation, the alternative hypothesis has an inequality. It is presented as H a or H 1 . The alternative hypothesis will be demonstrated indirectly through hypothesis testing. If the null hypothesis is rejected then the alternative hypothesis is accepted (Lock et al.,l 2012). for instance if the researcher reject the claim that police in low income areas does not experience the same blood pressure rate with the officers serving in the general population will have the alternative hypothesis as police officer serving the general population has the same blood pressure level with those in the low income areas.
The following formula is common in hypothesis testing
Null hypothesis: x is equal to Y
Alternative hypothesis: x is not equal to y.
References
Lock, R. H., Lock, P. F., Morgan, K. L, Lock, E. F., & Lock,D. F. (2012). Statistics: Unlocking the power of data. Hoboken, NJ: John Wiley & Sons, Inc. ISBN: 978-1-118-56855-2
