The Hypothesis Testing Framework is a statistical tool used to infer the validity of a hypothesis made regarding a data sample. It is primarily utilized to determine if patterns and results observed in data are due to chance or if they are statistically significant. This framework helps in decision-making processes by providing a structured way to validate assumptions and can be crucial in fields such as science, business, and social research.
Define the null and alternative hypotheses. | Choose a significance level (commonly set at 0.05). | Select the appropriate test based on the data and hypothesis. | Calculate the test statistic and p-value. | Compare the p-value with the significance level to decide whether to reject the null hypothesis.
Ensure the data sample is representative of the population. | Use the correct test for the data type and distribution. | Adjust significance levels when conducting multiple comparisons to avoid Type I errors.
Provides a clear and quantitative method for decision making. | Reduces bias by relying on statistical evidence. | Applicable across various fields and types of data.
Requires a clear understanding of statistical methods. | Can be sensitive to assumptions made in the analysis. | May not be suitable for small sample sizes.
Testing new theories or products in scientific and market research. | Evaluating the effectiveness of new policies or interventions.
When sample sizes are too small to yield reliable results. | When the data does not meet the assumptions of the chosen statistical test.