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Role of Hypothesis :

(1) Hypothesis helps to guide the investigator in the right direction.

(2) What is to be studied is clear to the researcher through hypothesis.

(3) The type of research be it exploratory, descriptive or causal is decided by the hypothesis

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(4) Statistical techniques are determined.

Procedure for Testing of Hypothesis :

(1) State the Null Hypothesis as well as the alternative hypothesis : In the context of statistical analysis, we often talk about null hypothesis and alternative hypothesis.

Ø If we are to compare A method B with method about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as null hypothesis. The null hypothesis is generally H0 .symbolized as Null hypothesis states that there is no difference between the parameter and the statistic that is being compared.

Ø As against this, we may think that the method A is superior or the method B is inferior, we are then stating what is termed as alternative hypothesis. The alternative hypothesis is generally symbolized as Ha.

Example : For example, let us assume the population mean=100 and set up the hypothesis µ=100.

This is called the null hypothesis and is denoted as;

Null Hypothesis         Ho:µ= µ H0 =100

If our sample results do not support this null hypothesis, we should conclude that something else is true. What we conclude rejecting the null hypothesis is known as alternative hypothesis. If we accept H0 then we are rejecting Ha and if we reject Ho then we are accepting Ha .For Ho:µ= µ H0 =100, we may consider three possible alternative

hypothesis as follows:

(2) Establish a level of Significance :This is a very important concept in the context of hypothesis testing. The level of significance signifies the probability of committing Type 1 error a is generally taken equal to 0.05. Sometimes the value is established as 0.01. , but it is at the discretion of the investigator to select its value, depending upon the sensitivity of the study. To illustrate per cent level of significance indicates that a researcher is willing to take 5 per cent risk of rejecting the Null Hypothesis when it happens to be true.

(3) Choosing a suitable test statistic : Now the researcher would choose amongst the various tests. Actually for the purpose of rejecting or accepting the null hypothesis, a suitable statistic called 'test statistics' is chosen. There are the following tests:

Ø Anova

Ø Chi-Square Test

Ø T-Test

Ø Other non parametric tests

(4) Selecting a random sample and computing an appropriate value :

Another step is to select a random sample and compute an appropriate value from the sample data concerning the test statistic utilizing the relevant distribution. In other words, draw a sample to furnish empirical data.

(5) Calculation of the probability : One has then to calculate the probability that the sample result would diverge as widely as it has from expectations, if null hypothesis were in fact true.

(6) Comparing the probability : Yet another step consists in comparing the probability thus calculated with the specified value for a, the significance level.

Ø If the calculated probability is equal to or smaller than the a value in case of one-tailed test, then reject the null hypothesis and accept the alternative hypothesis.

Ø If the calculated probability is greater than the a value in case of one-tailed test, then accept the null hypothesis and reject the alternative hypothesis
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