Explain the procedure for Testing Hypothesis.

Explain the procedure for Testing Hypothesis.
Answer:
A hypothesis is an assumption about relations between variables. It is a tentative explanation of the research problem or a guess about the research outcome.

To test a hypothesis means to tell (on the basis of the data researcher has collected) whether or not the hypothesis seems to be valid. The various steps involved in hypothesis testing are stated below:

a)    Making a Formal Statement

•    The step consists in making a formal statement of the null hypothesis (H) and also of the alternative hypothesis (H). This means that hypothesis should clearly state, considering the nature of the research problem.
•    The formulation of hypothesis is an important step which must be accomplished with due care in accordance with the object and nature of the problem under consideration. It also indicates whether we should use a tailed test or a two tailed test. If H is of the type greater than, we use alone tailed test, but when H is of the type “whether greater or smaller” then we use a two-tailed test.

b)    Selecting a Significant Level

•    The hypothesis is tested on a pre-determined level of significance and such the same should have specified. Generally, in practice, either 5% level or 1% level is adopted for the purpose.
•    The factors that affect the level of significance are:

• The magnitude of the difference between samples.
• The size of the sample.
• The variability of measurements within samples.
• Whether the hypothesis is directional or non – directional (A directional hypothesis is one which predicts the direction of the difference between say, means).

•    In brief, the level of significance must be adequate in the context of the purpose and nature of enquiry.

c)    Deciding the Distribution to Use

•    After deciding the level of significance, the next step in hypothesis testing is to determine the appropriate sampling distribution. The choice generally remains between distribution and the t distribution. The rules for selecting the correct distribution are similar to those which we have stated earlier in the context of estimation.

d)    Selecting A Random Sample & Computing An Appropriate Value

•    Another step is to select a random sample(S) 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.

e)    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 the null hypothesis were in fact true.

f)    Comparing the Probability

•    Yet another step consists in comparing the probability thus calculated with the specified value for a, the significance level.
•    If the calculated probability is equal to smaller than a value in case of one tailed test (and a/2 in case of two-tailed test), then reject the null hypothesis (i.e. accept the alternative hypothesis), but if the probability is greater then accept the null hypothesis. In case we reject H we run a risk of (at most level of significance) committing an error of type I, but if we accept H0, then we run some risk of committing error type II.