We now summarise below the various steps in testing of a statistical hypothesis in a systematic manner.

- Null hypothesis: set up the null hypothesis H0.
- Alternative hypothesis: set up the alternative hypothesis H1. This will enable us to decide whether we have to use a single tailed test or two tailed test.
- Level of significance: choose the appropriate level of significance (α) depending on the reliability of the estimates and permissible risk. This is to be decided before sample is drawn, that is α is fixed in advance.
- Test statistic: compute the test statistic

Under the null hypothesis. - Conclusion: we compare z the computed value of z in step 4 with the significant value zα, at the given level of significance, ‘α’.

If |Z| < zα, that is if the calculated value of Z is less than zα we say it is not significant. By this we mean that the difference t – E(t) is just due to fluctuations of sampling and the sample data do not provide us sufficient evidence against the null hypothesis which may therefore, be accepted.

If |Z| > zα, that is if the computed value of test statistic is greater than the critical or significant value, then we say that it is significant and the null hypothesis is rejected at level of significance α that is with confidence coefficient (1- α).

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- Procedure for testing of hypothesis
- Test Of Significance For Large Samples
- Sampling Of Attributes
- Test of significance for difference of proportions
- Sampling of Variables
- Standard Error Of Sample Mean
- Test Of Significance For Single Mean
- Test Of Significance For Difference Of Means
- Test Of Significance For The Difference Of Standard Deviations

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