**Introduction**

The concepts of null and alternative hypothesis are used while testing of statistical measurements of elements. Both the hypothesis looks similar but is different from each other. Following is a brief explanation of the same:

**Null Hypothesis**

In statistics, a null hypothesis is used in the form of a general statement or as a default position to infer the absence of any relationship between the two elements of a measurable phenomenon or that any medical treatment may not have any potential effect. Using the null hypothesis as a base to prove there cannot be any relationship between the two phenomena is one of the most integral parts of modern science and used to prove that a claim can be proved to be false.

A null hypothesis, in statistics, is referred by the symbol H? and the mathematical formulation is typically the sign of an equal. The hypothesis is generally considered to be true if the experiment and the supporting evidences prove something else.

In cases where the null hypothesis is found to be rejected, the user must be careful in stating what the inference of this situation can mean. It should be as measured as while giving legal statement or verdict. Say, for instance, a person will not be considered as innocent if the legal document certifies him as the same and in a very similar way, rejection of a null hypothesis does not state that the referring statement is untrue.

While studying any course of treatment that is relatively new, the null hypothesis should be that the subject matter does not change in any meaningful way because of the new working hypothesis.

**Alternative Hypothesis**

Alternative hypothesis is a concept and integral part of statistical hypothesis testing process. It is majorly used in contrast to null hypothesis as rival hypotheses. Also known as experimental hypothesis, it is used to refer to the observed effect that is reflected on the experiment that is being done. The alternative hypothesis is depicted mathematically through the symbols of inequality or simply, the symbol of not equal.

When the null hypothesis is rejected in the process of proving an experiment, the alternative hypothesis is accepted. Similarly, the alternative hypothesis is found to be rejected if the null hypothesis is found suitable and is accepted.

If anyone is studying any new course of treatment, having the alternative hypothesis helps in measuring the subject matter in a much more meaningful way.

**Negation**

During the formation of both null and alternative hypothesis, the following sets of negations may help in understanding and balancing the viewpoints.

If the null hypothesis is a and is equal to b, the alternative hypothesis is a is not equal to b. This is the most commonly used statement not only in statistics but also in other technical papers.

If the null hypothesis is a and is at least b, the alternative hypothesis is a is less than b.

The third scenario is that, if the null hypothesis is a is at most b, the alternative hypothesis is a is greater than b.