Hypothesis testing is a statistical method used to determine the validity of a claim regarding a population parameter based on sample data. The process involves formulating null and alternative hypotheses, determining the level of significance, choosing a test statistic, and making a decision based on the comparison of the test statistic with the critical value. The outcome indicates whether there's sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis.

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:
1. Formulate a null hypothesis (H0) and an alternative hypothesis (Ha).
2. Determine the level of significance (α)
3. Choose a test statistic and calculate its value.
4. Compare the test statistic to the critical value.
5. Make a decision and interpret the results.

4. 1. FORMULATION 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 rejected, 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.