In a randomized block experiment, the statement that is initially assumed to be true, and against which evidence is weighed, posits that there is no difference in the average treatment effects across the different treatment groups. Specifically, it asserts that any observed variations in the outcomes are due to random chance or inherent variability within the experimental units, rather than a genuine effect of the treatments being compared. For example, in an agricultural study examining the yield of different fertilizer types applied to various plots of land (blocks), the initial presumption is that all fertilizers have the same effect on yield, and any differences are merely due to variations in soil quality or other random factors.
The importance of this initial assertion lies in its role as a foundation for statistical inference. By establishing this initial presumption, researchers can then use statistical tests to determine whether the collected data provides sufficient evidence to reject it in favor of an alternative hypothesis, which posits that there is a real difference among the treatments. The controlled blocking aspect helps reduce variability, making it more likely to detect treatment effects if they exist. Historically, such hypothesis testing has been a cornerstone of scientific inquiry, ensuring that conclusions are grounded in empirical evidence rather than conjecture.
