What does the 'null hypothesis' propose in statistical testing?

The null hypothesis proposes that there is no effect or difference between the groups being studied. In the context of statistical testing, it serves as a starting point for hypothesis testing, allowing researchers to assess the evidence against it. The null hypothesis is essential in determining whether any observed effect in the data can be attributed to chance or if there is sufficient evidence to suggest that a significant difference exists.

When conducting experiments, researchers aim to gather data to either reject or fail to reject the null hypothesis. By establishing the null hypothesis as a baseline assumption, researchers can apply statistical tests to evaluate the likelihood of observing the gathered data if the null hypothesis were true. This process helps to clarify whether findings in the data warrant conclusions about real differences or effects, thus guiding further investigation and understanding within the field.

This framework is vital in oncology data work, where understanding effects can guide treatment decisions and research directions. The other options describe different aspects of statistical analysis that do not specifically define the null hypothesis.