Hypothesis testing is a fundamental statistical method used to make inferences or draw conclusions about a population based on sample data.
It involves formulating and testing hypotheses to determine if there is enough evidence to support a particular claim about a population parameter.
Steps in Hypothesis Testing
1. Formulate Hypotheses
Define the null and alternative hypotheses.
Null Hypothesis (H0): There is no effect or difference.
Alternative Hypothesis (H1): There is an effect or difference.
2. Choose Significance Level (α):
Decide on the level of significance, usually 0.05, which defines the probability of rejecting the null hypothesis when it is true.
3. Collect Data:
Conduct the experiment or study and gather sample data.
4. Perform Statistical Test
Use an appropriate statistical test (e.g., t-test, ANOVA) to calculate the test statistic and p-value.
5. Decision Rule
Compare the p-value to the significance level.
If p ≤ α: Reject the null hypothesis.
If p > α: Fail to reject the null hypothesis.
6. Draw Conclusion:
Based on the decision, conclude whether there is sufficient evidence to support the alternative hypothesis.