What is a null hypothesis in experimental research?

What is a null hypothesis in experimental research?

• A. A claim that there is an effect or a difference
• B. A statement that there is no effect or no difference
• C. A hypothesis that predicts outcomes based on prior studies
• D. A conclusion drawn after the data has been analyzed

Answer: (B)

In experimental research, the null hypothesis plays a fundamental role as it serves as a baseline or starting point for statistical testing. It is formulated to propose that there is no effect or no difference regarding the variables being tested. This means that any observed difference in the data is attributed to chance rather than a specific experimental intervention or factor. The purpose of the null hypothesis is to provide a clear framework for testing whether there is sufficient evidence to reject it in favor of an alternative hypothesis, which suggests that there is a significant effect or difference. By establishing this hypothesis, researchers can utilize statistical methods to determine the likelihood that the observed effects in the data could occur if the null were true. This understanding is crucial for conducting rigorous experiments and making objective conclusions based on the data, thereby preventing bias or subjective interpretation of results.

In experimental research, the null hypothesis plays a fundamental role as it serves as a baseline or starting point for statistical testing. It is formulated to propose that there is no effect or no difference regarding the variables being tested. This means that any observed difference in the data is attributed to chance rather than a specific experimental intervention or factor.

The purpose of the null hypothesis is to provide a clear framework for testing whether there is sufficient evidence to reject it in favor of an alternative hypothesis, which suggests that there is a significant effect or difference. By establishing this hypothesis, researchers can utilize statistical methods to determine the likelihood that the observed effects in the data could occur if the null were true.

This understanding is crucial for conducting rigorous experiments and making objective conclusions based on the data, thereby preventing bias or subjective interpretation of results.