LOGIC OF HYPOTHESIS TESTING.pptx

Hypothesis Testing and its process which includes the following steps:
1.Formulation of a null hypothesis (H0) and an alternative hypothesis (Ha).
2. Determination the level of significance (α)
3. Choosing a test statistic and calculate its value.
4. Comparison between the test statistic and the critical value.
5. Making a decision and interpret the results.
This is a summary of the whole process along with easy definitions of the associated terms.

1. LOGIC OF HYPOTHESIS TESTING COURSE – GEN341 SUBMITTED BY – SHARANYA (12108335)

2. WHAT IS HYPOTHESIS TESTING Hypothesis testing is a statistical method used in research to determine the validity of a claim or a statement about a population parameter based on a sample of data by  measuring and examining a random sample of the population being analyzed . In hypothesis testing, the goal is to determine whether the sample data provides sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The decision to accept or reject the null hypothesis is based on the calculated test statistic and the level of significance set by the researcher.

3. STEPS The logic behind hypothesis testing is based on the following steps : Formulate a null hypothesis (H0) and an alternative hypothesis (Ha ). Determine the level of significance (α ) Choose a test statistic and calculate its value. Compare the test statistic to the critical value . Make a decision and interpret the results.

4. 1. Formulat ION of a null hypothesis (H0) and an alternative hypothesis (Ha). The null hypothesis represents the status quo, and states that there is no significant difference between the population parameter and a specific value (usually the value of zero or a certain reference value). The alternative hypothesis , on the other hand, represents the researcher's claim and states that there is a significant difference between the population parameter and the null hypothesis value.

5. 2. Determination of the level of significance (α) This is the probability of making a type I error. Type 1 Error - The error of rejecting a true null hypothesis. Levels of significance lie between 0 and 1. This is typically done by calculating a test statistic (Z-score) and comparing it to a critical value from a statistical table based on the chosen level of significance and the degree of freedom of the data. If the calculated test statistic is greater than the critical value, the null hypothesis is rejected, and the results are considered statistically significant. If the calculated test statistic is less than or equal to the critical value, the null hypothesis cannot be rejected, and the results are considered not statistically significant.

6. 4. Choosing of a test statistic and calculation of its value The test statistic is a measure of how different the sample mean is from the null hypothesis value. The value of the test statistic is calculated based on the sample data and a specific distribution (such as the normal distribution ). Generally, the test statistic is calculated as  the pattern in your data (i.e. the correlation between variables or difference between groups) divided by the variance in the data (i.e. the standard deviation ) .

7. Making a Decision and Result Interpretation CASE 1 - If the calculated test statistic is greater than the critical value , the null hypothesis is rejected and the alternative hypothesis is accepted . This means that there is sufficient evidence to support the researcher's claim CASE 2 - If the calculated test statistic is less than or equal to the critical value , the null hypothesis cannot be rejec ted, and the results are considered not statistically significant .

8. CONCLUSION AND SUMMARY In summary, hypothesis testing is a logical and step-wise process used to determine the validity of a claim or a statement about a population parameter based on a sample of data. The decision to accept or reject the null hypothesis is based on the comparison of the calculated test statistic to the critical value, and the level of significance set by the researcher.