Hypothesis

Hypothesis

A hypothesis is defined as a proposition which can be put to test for determining the validity. That is s hypothesis is a tentative generalisation, the validity of which remains to be tested. Quite often a research hypothesis is a predictive statement, capable of being tested by scientific methods that relate an independent variable to some dependent variable. We may conclude that a hypothesis states what we are looking for and its is a proposition which can put to a test to determine its validity. Testing of the hypothesis is a statistical method/procedure of making an inference, about the population under study from the sample observation by testing it whether it is rejected or accept. It may also be defined as a statistical which is employed to make the decision on the estimates of parameters by testing the validity of the estimation made. It helps to make the decision whether to reject or to accept the hypothesis being tested, on the basis of sample data.

If the sample data provide sufficient evidence against the hypothesis tested, the hypothesis will be rejected, otherwise, the hypothesis will be accepted.

Null hypothesis.

If we are to compare method A with method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as a hypothesis.

The null hypothesis is defined as a hypothesis no difference, which is under test or verification. It is denoted by H₀. The null hypothesis is the hypothesis which is tested for possible rejection under the assumption that it is true. It is tested for possible rejection on the basis of sample observations drawn from the population. If the sample data do not provide sufficient evidence against H₀, we reject the H₀ and accept the alternative hypothesis.

H₀:v=v₀ where v₀ is some specified value of (θ).

Suppose we want to test the hypothesis than the population mean (µ) is equal to the hypothesis mean (µ₀)=100. Then we would say that the null hypothesis is that the population mean is equal to the hypothesis mean 100 and symbolically we can express as H₀=(µ)=(µ₀)=100.

Alternative hypothesis.

A complementary hypothesis to the null hypothesis is called the alternative hypothesis. Now suppose the null hypothesis is rejected and then another hypothesis must be accepted. So, another hypothesis must be stated along with the statements of Null hypothesis. This hypothesis stated is called the Alternative hypothesis. It is denoted by H₁.

If alternative hypothesis is stated without pointing to a definite direction of the difference, it is called two tails. If it points only one direction, it is called one tail.

An alternative hypothesis is also called hypothesis differences.

For example, if we are interested in testing the null hypothesis that the population mean has a specified value (µ₀) then the alternative hypothesis is set up as follows.

H0=(µ)=(µ₀). That is population mean has specified value µ₀. We will consider three possible alternative hypothesis.

1. H₁: µ ≠ µ_0.That is population mean is not equal to (µ₀). In other words, there is significance difference between the sample statistics (I,e sample mean ) and the population parameter (Population mean).
2. H₁. µ>µ_0.That a population mean is greater than (µ₀).
3. H1 . µ< µ_0.That is population mean is less than (µ₀).

Out of all the above three possible hypothesis , only one alternative hypothesis is selected depending upon the nature of the problem involving the parameter to which the null hypothesis is related.

Suppose we want to set up alternative hypothesis against the null hypothesis that the population height of Nepalese people is 5.2ft. We set up the null hypothesis and alternative hypothesis as follows.

H0=µ=5.2ft.

Then the alternative hypothesis is.

H1:µ≠5.2ft. That is population mean height of Nepalese people is not equal to 5.2ft. In other words, there are significance differences between sample mean height and population Nepalese people.

$$H_1>5.2Ft\,That\,is\,population\,mean\,height\,isgreater\,than\,than\,5.2ft$$

$$H_1<5.2Ft\,That\,is\,population\,mean\,height\,is\,less\,than\,than\,5.2ft$$

The importance of hypothesis.

The importance of the hypothesis is as listed as below points.

1. It determines the method of verification as well as the procedure of enquiry.
2. It adequately explains all the facts connected with the research.
3. It helps in deciding the direction in which to proceed.
4. It suggests experiments and observation.
5. It leads to the discovery of lens.
6. It helps to draw a specific conclusion.

Characteristics of a hypothesis.

The main characteristics of a hypothesis are as below.

1. The most important condition for a valid hypothesis is that it should be empirically verified. A hypothesis ultimately has to confirm or refuge, otherwise, it will remain more supposition I,e it must be capable empirically tested under the condition of available technique.
2. The hypothesis should state the relationship between variable.
3. The hypothesis should state the relationship between variable.
4. The hypothesis should be limited in scope and must be specified.
5. The hypothesis should be limited clear, definite and non-ambiguous.
6. The hypothesis should be stated as far as possible in most simple language so that someone easily understands by all concern.
7. It is desirable that hypothesis is selected must be in continuation with the theory already involved I,e the hypothesis selected should be related to the body of a theory.

Reference

Kerlinger, F.N. Foundation of Behavioural Research. New Delhi: Surjeet Publication, 2000.

Kothari, C.R. Research Methodology. India: Vishwa Prakashan, 1990.

Singh, M.L. and J.M Singh. Understanding Research Methodology. 1998.

Singh, Mrigendra Lal. Understanding Research Methodology. Nepal: National Book centre, 2013.